Methods for profiling and quantitating cell-free rna

ABSTRACT

The invention generally relates to methods for assessing a neurological disorder by characterizing circulating nucleic acids in a blood sample. According to certain embodiments, methods for assessing a neurological disorder include obtaining RNA present in a blood sample of a patient suspected of having a neurological disorder, determining a level of RNA present in the sample that is specific to brain tissue, comparing the sample level of RNA to a reference level of RNA specific to brain tissue, determining whether a difference exists between the sample level and the reference level, and indicating a neurological disorder if a difference is determined.

RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. Provisional No. 61/900,927, filed Nov. 6, 2013, and is a continuation-in-part of U.S. Non-Provisional Ser. No. 13/752,131, filed Jan. 28, 2013, which claims the benefit of and priority to U.S. Provisional No. 61/591,642, filed on Jan. 27, 2012. The entirety of each foregoing application is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to assessing neurological disorders based on nucleic acid specific to brain tissue.

BACKGROUND

Dementia is a catchall term used to characterize cognitive declines that interfere with one's ability to perform everyday activities. Signs of dementia include declines in the following mental functions: memory, communication and language, ability to focus and pay attention, reasoning, judgment, motor skills, and visual perception. While there are several neurological disorders that cause dementia, Alzheimer's disease is the most common, accounting for 60 to 80 percent of all dementia cases.

Alzheimer's disease is a progressive disease that gradually destroys memory and mental functions in patients. Symptoms manifest initially as a decline in memory followed by deterioration of other cognitive functions as well as by abnormal behavior. Individuals with Alzheimer's disease usually begin to show dementia symptoms later in life (e.g., 65 years or older), but a small percentage of individuals in their 40s and 50s experience early onset Alzheimer's disease. Alzheimer's disease is associated with the damage and degeneration of neurons in several regions of the brain. The neuropathic characteristics of Alzheimer's disease include the presence of plaques and tangles, synaptic loss, and selective neuronal cell death. Plaques are abnormal levels of protein fragments called beta-amyloid that accumulate between nerve cells. Tangles are twisted fibers of a protein known as tau that accumulate within nerve cells.

While the above-described neuropathic characteristics are hallmarks of the disease, the exact cause of Alzheimer's disease is unknown and there are no specific tests that confirm whether an individual has Alzheimer's disease. For diagnosis of Alzheimer's, clinicians assess a combination of clinical criteria, which may include a neurological exam, mental status tests, and brain imaging. Efforts are being made to determine the genetic causes in order to help definitively diagnose Alzheimer's disease. However, only a handful of genetic markers associated with Alzheimer's have been characterized to date, and diagnostic tests for those markers require invasive brain biopsies.

SUMMARY

The present invention provides methods for assessing neurological conditions using circulating nucleic acid (such as DNA or RNA) that is specific to brain tissue. In particular embodiments, the invention involves a comparative analysis of levels of circulating nucleic acid in a patient that are specific to brain tissue with reference levels of circulating nucleic acid that are specific to brain tissue. The present invention recognizes that abnormal deviations in circulating nucleic acid result from tissue-specific nucleic acid being released into the blood in large amounts as tissue begins to fail and degrade. By focusing on genes the expression of which is highly specific to brain tissue, methods of the invention allow one to characterize the extent of brain degradation based on statistically-significant levels of circulating brain-specific transcripts; and use that characterization to diagnose and determine the stage of the neurological disease. Accordingly, methods of the invention allow one to characterize neurological disorders without focusing on small subset of known biomarkers, but rather focusing on the extent to which nucleic acid is released into blood from brain tissue affected by disease. Methods of the invention are particularly useful in diagnosing and determining the stage of Alzheimer's disease.

In particular embodiments, methods of the invention include obtaining RNA from a blood sample of a patient suspected of having a neurological disorder, and determining a level of the sample RNA that originated from brain tissue. In certain embodiments, the RNA is converted to cDNA. The level of the sample RNA specific to brain tissue is then compared to a reference level of RNA that is specific to brain tissue. The reference level may be derived from a subject or patient population having a neurological disorder or from a normal/control subject or patient population. Depending on the reference level chosen, similarities or variances between the level of sample RNA and the reference level of RNA are indicative of the neurological disorder, the type of neurological disorder and/or the stage of the neurological disorder. In certain embodiments, only similarities or variances of statistical significance are indicative of the neurological disorder. Whether a variance is significant depends upon the chosen reference population.

Additional aspects of the invention involve assessing a neurological disorder using a set of predictive variables correlated with a neurological disorder. In such aspects, methods of the invention involve detecting RNA present in a biological sample obtained from a patient suspected of having a neurological disorder. In certain embodiments, the RNA is converted to cDNA. Sample levels of one or more RNA transcripts that are specific to brain tissue are determined, and the sample levels of RNA transcripts specific to brain tissue are compared to a set of predictive variables correlated with a neurological disorder. The predictive variables may include reference levels of RNA transcripts that are specific to brain tissue and correspond to one or more stages of the neurological disorders. In certain embodiments, the predictive variables may include brain-specific reference levels of transcripts that correlate to other factors such as age, sex, environmental exposure, familial history of dementia, dementia symptoms. The stage of a neurological disorder of the patient may be indicated based on variances or similarities between the level of sample RNA and the predictive variables.

RNA obtained from the blood sample may be converted into synthetic cDNA. In such instances, the sample levels of cDNA that correspond to RNA originating from brain tissue may be compared to reference levels of RNA or references levels of cDNA that correspond to RNA originating from brain tissue. For example, methods of the invention may include the steps of detecting circulating RNA in a sample obtained from a patient suspected of having a neurological disorder and converting the circulating RNA from the sample into cDNA. The next steps involve determining levels of the sample cDNA that correspond to RNA originating from brain tissue, and comparing the determined levels of the cDNA to a reference level of cDNA. The reference level of cDNA may also correspond to RNA originating from brain tissue. The neurological condition of the patient may then be indicated based similarities or differences between the patient cDNA levels and the reference cDNA levels.

Methods of the invention are also useful to identify one or more biomarkers associated with a neurological disorder. In such aspects, brain-specific transcripts of an individual or patient population suspected of having or actually having a neurological disorder (e.g. exhibiting impaired cognitive functions) are compared to a reference (e.g. brain-specific transcripts of a healthy, normal population). The brain-specific transcripts of the individual or patient population that are differentially expressed as compared to the reference may then be identified as biomarkers of the neurological disorder. In certain embodiments, only differentially expressed brain-specific transcripts that are statistically significant are identified as biomarkers. Methods of determining statistical significance are known in the art.

The reference level of RNA or cDNA specific to brain tissue may pertain to a patient population having a particular condition or pertain to a normal/control patient population. In one embodiment, the reference level of RNA or cDNA specific to brain tissue may be levels of RNA or cDNA specific to brain tissue in a normal patient population. In another embodiment, the reference level of RNA or cDNA may be the level of RNA or cDNA specific to brain tissue in a patient population having a certain neurological disorder. The certain neurological disorder may be mild cognitive impairment or moderate-to-severe cognitive impairment. The various levels of cognitive impairment may be indicative of a stage of Alzheimer's disease. In further embodiments, the reference level of RNA or cDNA may be the level of RNA or cDNA specific to brain tissue having a certain neurological disorder at a certain age. Other embodiments may include reference levels that correspond to a variety of predictive variables, including type of neurological disorder, stage of neurological disorder, age, sex, environmental exposure, familial history of dementia, dementia symptoms.

Methods of the invention involve assaying biological samples for circulating nucleic acid (RNA or DNA). Suitable biological samples may include blood, blood fractions, plasma, saliva, sputum, urine, semen, transvaginal fluid, and cerebrospinal fluid. Preferably, the sample is a blood sample. The blood sample may be plasma or serum.

The present invention also provides methods for profiling the origin of the cell-free RNA to assess the health of an organ or tissue. Deviations in normal cell-free transcriptomes are caused when organ/tissue-specific transcripts are released in to the blood in large amounts as those organs/tissue begin to fail or are attacked by the immune system or pathogens. As a result inflammation process can occur as part of body's complex biological response to these harmful stimuli. The invention, according to certain aspects, utilizes tissue-specific RNA transcripts of healthy individuals to deduce the relative optimal contributions of different tissues in the normal cell-free transcriptome, with each tissue-specific RNA transcript of the sample being indicative of the apotopic rate of that tissue. The normal cell-free transcriptome serves as a baseline or reference level to assess tissue health of other individuals. The invention includes a comparative measurement of the cell-free transcriptome of a sample to the normal cell free transcriptome to assess the sample levels of tissue-specific transcripts circulating in plasma and to assess the health of tissues contributing to the cell-free transcriptome.

In addition to cell-free transcriptomes reference levels of normal patient populations, methods of the invention also utilize reference levels for cell-free transcriptomes specific to other patient populations. Using methods of the invention one can determine the relative contribution of tissue-specific transcripts to the cell-free transcriptome of maternal subjects, fetus subjects, and/or subjects having a condition or disease.

By analyzing the health of tissue based on tissue-specific transcripts, methods of the invention advantageously allow one to assess the health of a tissue without relying on disease-related protein biomarkers. In certain aspects, methods of the invention assess the health of a tissue by comparing a sample level of RNA in a biological sample to a reference level of RNA specific to a tissue, determining whether a difference exists between the sample level and the reference level, and characterizing the tissue as abnormal if a difference is detected. For example, if a patient's RNA expression levels for a specific tissue differs from the RNA expression levels for the specific tissue in the normal cell-free transcriptome, this indicates that patient's tissue is not functioning properly.

In certain aspects, methods of the invention involve assessing health of a tissue by characterizing the tissue as abnormal if a specified level of RNA is present in the blood. The method may further include detecting a level of RNA in a blood sample, comparing the sample level of RNA to a reference level of RNA specific to a tissue, determining whether a difference exists between the sample level and the reference level, and characterizing the tissue as abnormal if the sample level and the reference level are the same.

The present invention also provides methods for comprehensively profiling fetal specific cell-free RNA in maternal plasma and deconvoluting the cell-free transcriptome of fetal origin with relative proportion to different fetal tissue types. Methods of the invention involve the use of next-generation sequencing technology and/or microarrays to characterize the cell-free RNA transcripts that are present in maternal plasma at different stages of pregnancy. Quantification of these transcripts allows one to deduce changes of these genes across different trimesters, and hence provides a way of quantification of temporal changes in transcripts.

Methods of the invention allow diagnosis and identification of the potential for complications during or after pregnancy. Methods also allow the identification of pregnancy-associated transcripts which, in turn, elucidates maternal and fetal developmental programs. Methods of the invention are useful for preterm diagnosis as well as elucidation of transcript profiles associated with fetal developmental pathways generally. Thus, methods of the invention are useful to characterize fetal development and are not limited to characterization only of disease states or complications associated with pregnancy. Exemplary embodiments of the methods are described in the detailed description, claims, and figures provided below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a listing of the top detected female pregnancy associated differentially expressed transcripts.

FIG. 2 shows plots of the two main principal components for cell free RNA transcript levels obtained in Example 1.

FIG. 3A depicts a heatmap of the top 100 cell free transcript levels exhibiting different temporal levels in preterm and normal pregnancy using microarrays. The heat map of FIG. 3A is split across FIG. 3A-1 and FIG. 3A-2, as indicated by the graphical figure outline.

FIG. 3B depicts heatmap of the top 100 cell free transcript levels exhibiting different temporal levels in preterm and normal pregnancy using RNA-Seq. The heat map of FIG. 3B is split across FIG. 3B- and FIG. 3B-2, as indicated by the graphical figure outline.

FIG. 4 depicts a ranking of the top 20 transcripts differentially expressed between pre-term and normal pregnancy.

FIG. 5 depicts results of a Gene Ontology analysis on the top 20 common RNA transcripts of FIG. 4, showing those transcripts enriched for proteins that are attached (integrated or loosely bound) to the plasma membrane or on the membranes of the platelets.

FIG. 6 depicts that the gene expression profile for PVALB across the different trimesters shows the premature births [highlighted in blue] has higher levels of cell free RNA transcripts found as compared to normal pregnancy.

FIG. 7 outlines exemplary process steps for determining the relative tissue contributions to a cell-free transcriptome of a sample. FIG. 7 is split across FIGS. 7A and 7B, as indicated by the graphical figure outline.

FIG. 8 depicts the panel of selected fetal tissue-specific transcripts generated in Example 2. FIG. 8 is split across FIGS. 8A and 8B, as indicated by the graphical figure outline.

FIGS. 9A and 9B depict the raw data of parallel quantification of the fetal tissue-specific transcripts showing changes across maternal time-points (first trimester, second trimester, third trimester, and post partum) using the actual cell free RNA as well as the cDNA library of the same cell free RNA.

FIG. 10 illustrates relative expression of placental genes across maternal time points (first trimester, second trimester, third trimester, and post partum). FIG. 10 is split across FIGS. 10A and 10B, as indicated by the graphical figure outline. In FIG. 10, relative expression fold changes of each trimester as compared to post-partum for the panel of placental genes. Plotted are the results for two subjects done at two different concentrations each, each point represent one subject sampled at a particular trimester, and the cell free RNA went through the described protocol at two concentration levels. FIG. 10B depicts the same results segmented across the two subjects labeled as P53 & P54.

FIG. 11 illustrates relative expression of fetal brain genes across maternal time points (first trimester, second trimester, third trimester, and post partum). FIG. 11 is split across FIGS. 11A and 11B, as indicated by the graphical figure outline. In FIG. 11A, relative expression folds changes of each trimester as compared to post-partum for the panel of Fetal Brain genes. Plotted are the results for two subjects done at two different concentrations each, each point represent one subject sampled at a particular trimester, and the cell free RNA went through the described protocol at two concentration levels. FIG. 1B depicts the same results segmented across the two subjects labeled as P53 & P54.

FIG. 12 illustrates relative expression of fetal liver genes across maternal time points (first trimester, second trimester, third trimester, and post partum). FIG. 12 is split across FIGS. 12A and 12B, as indicated by the graphical figure outline. In FIG. 12A, relative expression fold changes of each trimester as compared to post-partum for the panel of Fetal Liver genes. Plotted are the results for two subjects done at two different concentrations each, each point represent one subject sampled at a particular trimester, and the cell free RNA went through the described protocol at two concentration levels. FIG. 12B depicts the same results segmented across the two subjects labeled as P53 & P54.

FIG. 13 illustrates the relative composition of different organs contribution towards a plasma adult cell free transcriptome.

FIG. 14 illustrates a decomposition of decomposition of organ contribution towards a plasma adult cell free transcriptome using RNA-seq data.

FIG. 15 shows a heat map of the tissue specific transcripts of Table 2 of Example 3, being detectable in the cell free RNA.

FIG. 16 depicts a flow-diagram of a method of the invention according to certain embodiments.

FIG. 17 illustrates identifying brain-specific cell-free RNA transcripts that differ between Alzheimer's subjects and control subjects.

FIG. 18 illustrates an experimental design comparing microarray, RNA-seq and quantitative PCR for a customized bioinformatics pipeline. In the experiment, 11 pregnant women and 4 non-pregnant control subjects were recruited. For all the pregnant patients, blood was drawn at 1st, 2nd, 3rd trimester and postpartum. The cell-free plasma RNA were then extracted, amplified and characterized by Affymetrix microarray, Illumina sequencer and quantitative PCR.

FIG. 19 illustrates a heat map of temporal varying genes obtained from microarray analysis. Unsupervised clustering was performed on genes across different time points. Cluster of genes belongs to the CGB family of genes which are known to be expressed at high levels during the first trimester exhibited corresponding high levels of RNA in the first trimester.

FIG. 20 illustrates another heat map of temporal varying genes obtained from microarray analysis. Unsupervised clustering was performed on genes across different time points. Cluster of genes belongs to the CGB family of genes which are known to be expressed at high levels during the first trimester exhibited corresponding high levels of RNA in the first trimester.

FIG. 21 illustrates a list of genes identified with fetal SNPs using the experimental design of FIG. 18. List of identified Gene Transcripts with identified fetal SNPs and the captured temporal dynamics. The barplot reflects the relative contribution of fetal SNPs as reflected in the sequencing data. The red color bar reflects the extent of the relative Fetal SNP contribution.

FIG. 22 identifies placental specific transcripts measured by qPCR in the experimental design of FIG. 18. As shown in FIG. 22, the time course of placental specific genes is measured by qPCR. Plot showing the Delta Ct value with respect to the housekeeping gene ACTB across the different trimesters of pregnancy including after birth. General trends show elevated levels during the trimesters with a decline to low levels after the baby is born.

FIG. 23 identifies fetal brain specific transcripts measured byq. As shown in FIG. 23, the time course of fetal brain specific genes is measured by qPCR. Plot showing the Delta Ct value with respect to the housekeeping gene ACTB across the different trimesters of pregnancy including after birth. General trends show elevated levels during the trimesters with a decline to low levels after the baby is born.

FIG. 24 identifies fetal liver specific transcripts measured by qPCR. As shown in FIG. 24, the time course of fetal liver specific genes is measured by qPCR. Plot showing the Delta Ct value with respect to the housekeeping gene ACTB across the different trimesters of pregnancy including after birth. General trends show elevated levels during the trimesters with a decline to low levels after the baby is born.

FIG. 25 illustrates tissue composition of the adult cell free transcriptome in typical adult plasma as a summation of RNAs from different tissue types.

FIG. 26 illustrates decomposition of Cell-free RNA transcriptome of normal adult into their respective tissues types using microarray data and quadratic programming.

FIG. 27 depicts a Principle Component Analysis (PCA) space reflecting the unsupervised clustering of the patients using the gene expression data from the 48 genes assay.

FIG. 28 depicts the measured APP levels in patients. The left panel shows the levels of APP transcripts across different age groups in the study. The right panel shows the different levels of the APP transcripts of the combined population of patients.

FIG. 29 depicts the measured MOBP levels in patients. The left panel shows the levels of the MOBP transcripts across different age groups in the study. The right panel shows the different levels of the MOBP transcripts of the combined population of patients.

FIG. 30 depicts classification results using combined Z-scores.

DETAILED DESCRIPTION

Methods and materials described herein apply a combination of next-generation sequencing and microarray techniques for detecting, quantitating and characterizing RNA present in a biological sample. In certain embodiments, the biological sample contains a mixture of genetic material from different genomic sources, i.e. pregnant female and a fetus.

Unlike other methods of digital analysis in which the nucleic acid in the sample is isolated to a nominal single target molecule in a small reaction volume, methods of the present invention are conducted without diluting or distributing the genetic material in the sample. Methods of the invention allow for simultaneous screening of multiple transcriptomes, and provide informative sequence information for each transcript at the single-nucleotide level, thus providing the capability for non-invasive, high throughput screening for a broad spectrum of diseases or conditions in a subject from a limited amount of biological sample.

In one particular embodiment, methods of the invention involve analysis of mixed fetal and maternal RNA in the maternal blood to identify differentially expressed transcripts throughout different stages of pregnancy that may be indicative of a preterm or pathological pregnancy. Differential detection of transcripts is achieved, in part, by isolating and amplifying plasma RNA from the maternal blood throughout the different stages of pregnancy, and quantitating and characterizing the isolated transcripts via microarray and RNA-Seq.

Methods and materials specific for analyzing a biological sample containing RNA (including non-maternal, maternal, maternal-fetus mixed) as described herein, are merely one example of how methods of the invention can be applied and are not intended to limit the invention. Methods of the invention are also useful to screen for the differential expression of target genes related to cancer diagnosis, progression and/or prognosis using cell-free RNA in blood, stool, sputum, urine, transvaginal fluid, breast nipple aspirate, cerebrospinal fluid, etc.

In certain embodiments, methods of the invention generally include the following steps: obtaining a biological sample containing genetic material from different genomic sources, isolating total RNA from the biological sample containing biological sample containing a mixture of genetic material from different genomic sources, preparing amplified cDNA from total RNA, sequencing amplified cDNA, and digital counting and analysis, and profiling the amplified cDNA.

Methods of the invention also involve assessing the health of a tissue contributing to the cell-free transcriptome. In certain embodiments, the invention involves assessing the cell-free transcriptome of a biological sample to determine tissue-specific contributions of individual tissues to the cell-free transcriptome. According to certain aspects, the invention assesses the health of a tissue by detecting a sample level of RNA in a biological sample, comparing the sample level of RNA to a reference level of RNA specific to the tissue, and characterizing the tissue as abnormal if a difference is detected. This method is applicable to characterize the health of a tissue in non-maternal subjects, pregnant subjects, and live fetuses. FIG. 16 depicts a flow-diagram of this method according to certain embodiments.

In certain aspects, methods of the invention employ a deconvolution of a reference cell-free RNA transcriptome to determine a reference level for a tissue. Preferably, the reference cell-free RNA transcriptome is a normal, healthy transcriptome, and the reference level of a tissue is a relative level of RNA specific to the tissue present in the blood of healthy, normal individuals. Methods of the invention assume that apoptotic cells from different tissue types release their RNA into plasma of a subject. Each of these tissues expresses a specific number of genes unique to the tissue type, and the cell-free RNA transcriptome of a subject is a summation of the different tissue types. Each tissue may express one or more numbers of genes. In certain embodiments, the reference level is a level associated with one of the genes expressed by a certain tissue. In other embodiments, the reference level is a level associated with a plurality of genes expressed by a certain tissue. It should be noted that a reference level or threshold amount for a tissue-specific transcript present in circulating RNA may be zero or a positive number.

For healthy, normal subjects, the relative contributions of circulating RNA from different tissue types are relatively stable, and each tissue-specific RNA transcript of the cell-free RNA transcriptome for normal subjects can serve as a reference level for that tissue. Applying methods of the invention, a tissue is characterized as unhealthy or abnormal if a sample includes a level of RNA that differs from a reference level of RNA specific to the tissue. The tissue of the sample may be characterized as unhealthy if the actual level of RNA is statistically different from the reference level. Statistical significance can be determined by any method known in the art. These measurements can be used to screen for organ health, as diagnostic tool, and as a tool to measure response to pharmaceuticals or in clinical trials to monitor health.

If a difference is detected between the sample level of RNA and the reference level of RNA, such difference suggests that the associated tissue is not functioning properly. The change in circulating RNA may be the precursor to organ failure or indicate that the tissue is being attacked by the immune system or pathogens. If a tissue is identified as abnormal, the next step(s), according to certain embodiments, may include more extensive testing of the tissue (e.g. invasive biopsy of the tissue), prescribing course of treatment specific to the tissue, and/or routine monitoring of the tissue.

Methods of the invention can be used to infer organ health non-invasively. This non-invasive testing can be used to screen for appendicitis, incipient diabetes and pathological conditions induced by diabetes such as nephropathy, neuropathy, retinopathy etc. In addition, the invention can be used to determine the presence of graft versus host disease in organ transplants, particularly in bone marrow transplant recipients whose new immune system is attacking the skin, GI tract or liver. The invention can also be used to monitor the health of solid organ transplant recipients such as heart, lung and kidney. The methods of the invention can assess likelihood of prematurity, preeclampsia and anomalies in pregnancy and fetal development. In addition, methods of the invention could be used to identify and monitor neurological disorders (e.g. multiple sclerosis and Alzheimer's disease) that involve cell specific death (e.g. of neurons or due to demyelination) or that involve the generation of plaques or protein aggregation.

A cell-free transcriptome for purposes of determining a reference level for tissue-specific transcripts can be the cell-free transcriptome of one or more normal subjects, maternal subjects, subjects having a certain conditions and diseases, or fetus subjects. In the case of certain conditions, the reference level of a tissue is a level of RNA specific to the tissue present in blood of one or more subjects having a certain disease or condition. In such aspect, the method includes detecting a level of RNA in a blood, comparing the sample level of RNA to a reference level of RNA specific to a tissue, determining whether a difference exists between the sample level and the reference level, and characterizing the as abnormal if the sample level and the reference level are the same.

A deconvolution of a cell-free transcriptome is used to determine the relative contribution of each tissue type towards the cell-free RNA transcriptome. The following steps are employed to determine the relative RNA contributions of certain tissues in a sample. First, a panel of tissue-specific transcripts is identified. Second, total RNA in plasma from a sample is determined using methods known in the art. Third, the total RNA is assessed against the panel of tissue-specific transcripts, and the total RNA is considered a summation these different tissue-specific transcripts. Quadratic programming can be used as a constrained optimization method to deduce the relative optimal contributions of different organs/tissues towards the cell-free transcriptome of the sample.

One or more databases of genetic information can be used to identify a panel of tissue-specific transcripts. Accordingly, aspects of the invention provide systems and methods for the use and development of a database. Particularly, methods of the invention utilize databases containing existing data generated across tissue types to identify the tissue-specific genes. Databases utilized for identification of tissue-specific genes include the Human 133A/GNF1H Gene Atlas and RNA-Seq Atlas, although any other database or literature can be used. In order to identify tissue-specific transcripts from one or more databases, certain embodiments employ a template-matching algorithm to the databases. Template matching algorithms used to filter data are known in the art, see e.g., Pavlidis P. Noble WS (2001) Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol 2: research0042.1-0042.15.

In certain embodiments, quadratic programming is used as a constrained optimization method to deduce relative optimal contributions of different organs/tissues towards the cell-free transcriptome in a sample. Quadratic programming is known in the art and described in detail in Goldfarb and A. Idnani (1982). Dual and Primal-Dual Methods for Solving Strictly Convex Quadratic Programs. In J. P. Hennart (ed.), Numerical Analysis, Springer-Verlag, Berlin, pages 226-239, and D. Goldfarb and A. Idnani (1983). A numerically stable dual method for solving strictly convex quadratic programs. Mathematical Programming, 27, 1-33.

FIG. 7 outlines exemplary process steps for determining the relative tissue contributions to a cell-free transcriptome of a sample. Using information provided by one or more tissue-specific databases, a panel of tissue-specific genes is generated with a template-matching function. A quality control function can be applied to filter the results. A blood sample is then analyzed to determine the relative contribution of each tissue-specific transcript to the total RNA of the sample. Cell-free RNA is extracted from the sample, and the cell-free RNA extractions are processed using one or more quantification techniques (e.g. standard mircoarrays and RNA-sequence protocols). The obtained gene expression values for the sample are then normalized. This involves rescaling of all gene expression values to the housekeeping genes. Next, the sample's total RNA is assessed against the panel of tissue-specific genes using quadratic programming in order to determine the tissue-specific relative contributions to the sample's cell-free transcriptome. The following constraints are employed to obtain the estimated relative contributions during the quadratic programming analysis: a) the RNA contributions of different tissues are greater than or equal to zero, and b) the sum of all contributions to the cell-free transcriptome equals one.

Method of the invention for determining the relative contributions for each tissue can be used to determine the reference level for the tissue. That is, a certain population of subjects (e.g., maternal, normal, cancerous, Alzheimer's (and various stages thereof)) can be subject to the deconvolution process outlined in FIG. 7 to obtain reference levels of tissue-specific gene expression for that patient population. When relative tissue contributions are considered individually, quantification of each of these tissue-specific transcripts can be used as a measure for the reference apoptotic rate of that particular tissue for that particular population. For example, blood from one or more healthy, normal individuals can be analyzed to determine the relative RNA contribution of tissues to the cell-free RNA transcriptome for healthy, normal individuals. Each relative RNA contribution of tissue that makes up the normal RNA transcriptome is a reference level for that tissue.

According to certain embodiments, an unknown sample of blood can be subject to process outlined in FIG. 7 to determine the relative tissue contributions to the cell-free RNA transcriptome of that sample. The relative tissue contributions of the sample are then compared to one or more reference levels of the relative contributions to a reference cell-free RNA transcriptome. If a specific tissue shows a contribution to the cell-free RNA transcriptome in the sample that is greater or less than the contribution of the specific tissue in a reference cell-free RNA transcriptome, then the tissue exhibiting differential contribution may be characterized accordingly. If the reference cell-free transcriptome represents a healthy population, a tissue exhibiting a differential RNA contribution in a sample cell-free transcriptome can be classified as unhealthy.

The biological sample can be blood, saliva, sputum, urine, semen, transvaginal fluid, cerebrospinal fluid, sweat, breast milk, breast fluid (e.g., breast nipple aspirate), stool, a cell or a tissue biopsy. In certain embodiments, the samples of the same biological sample are obtained at multiple different time points in order to analyze differential transcript levels in the biological sample over time. For example, maternal plasma may be analyzed in each trimester. In some embodiments, the biological sample is drawn blood and circulating nucleic acids, such as cell-free RNA. The cell-free RNA may be from different genomic sources is found in the blood or plasma, rather than in cells.

In a particular embodiment, the drawn blood is maternal blood. In order to obtain a sufficient amount of nucleic acids for testing, it is preferred that approximately 10-50 mL of blood be drawn. However, less blood may be drawn for a genetic screen in which less statistical significance is required, or in which the RNA sample is enriched for fetal RNA.

Methods of the invention involve isolating total RNA from a biological sample. Total RNA can be isolated from the biological sample using any methods known in the art. In certain embodiments, total RNA is extracted from plasma. Plasma RNA extraction is described in Enders et al., “The Concentration of Circulating Corticotropin-releasing Hormone mRNA in Maternal Plasma Is Increased in Preeclampsia,” Clinical Chemistry 49: 727-731, 2003. As described there, plasma harvested after centrifugation steps is mixed Trizol LS reagent (Invitrogen) and chloroform. The mixture is centrifuged, and the aqueous layer transferred to new tubes. Ethanol is added to the aqueous layer. The mixture is then applied to an RNeasy mini column (Qiagen) and processed according to the manufacturer's recommendations.

In the embodiments where the biological sample is maternal blood, the maternal blood may optionally be processed to enrich the fetal RNA concentration in the total RNA. For example, after extraction, the RNA can be separated by gel electrophoresis and the gel fraction containing circulatory RNA with a size of corresponding to fetal RNA (e.g., <300 bp) is carefully excised. The RNA is extracted from this gel slice and eluted using methods known in the art.

Alternatively, fetal specific RNA may be concentrated by known methods, including centrifugation and various enzyme inhibitors. The RNA is bound to a selective membrane (e.g., silica) to separate it from contaminants. The RNA is preferably enriched for fragments circulating in the plasma, which are less than less 300 bp. This size selection is done on an RNA size separation medium, such as an electrophoretic gel or chromatography material.

Flow cytometry techniques can also be used to enrich for fetal cells in maternal blood (Herzenberg et al., PNAS 76: 1453-1455 (1979); Bianchi et al., PNAS 87: 3279-3283 (1990); Bruch et al., Prenatal Diagnosis 11: 787-798 (1991)). U.S. Pat. No. 5,432,054 also describes a technique for separation of fetal nucleated red blood cells, using a tube having a wide top and a narrow, capillary bottom made of polyethylene. Centrifugation using a variable speed program results in a stacking of red blood cells in the capillary based on the density of the molecules. The density fraction containing low-density red blood cells, including fetal red blood cells, is recovered and then differentially hemolyzed to preferentially destroy maternal red blood cells. A density gradient in a hypertonic medium is used to separate red blood cells, now enriched in the fetal red blood cells from lymphocytes and ruptured maternal cells. The use of a hypertonic solution shrinks the red blood cells, which increases their density, and facilitates purification from the more dense lymphocytes. After the fetal cells have been isolated, fetal RNA can be purified using standard techniques in the art.

Further, an agent that stabilizes cell membranes may be added to the maternal blood to reduce maternal cell lysis including but not limited to aldehydes, urea formaldehyde, phenol formaldehyde, DMAE (dimethylaminoethanol), cholesterol, cholesterol derivatives, high concentrations of magnesium, vitamin E, and vitamin E derivatives, calcium, calcium gluconate, taurine, niacin, hydroxylamine derivatives, bimoclomol, sucrose, astaxanthin, glucose, amitriptyline, isomer A hopane tetral phenylacetate, isomer B hopane tetral phenylacetate, citicoline, inositol, vitamin B, vitamin B complex, cholesterol hemisuccinate, sorbitol, calcium, coenzyme Q, ubiquinone, vitamin K, vitamin K complex, menaquinone, zonegran, zinc, Ginkgo biloba extract, diphenylhydantoin, perftoran, polyvinylpyrrolidone, phosphatidylserine, tegretol, PABA, disodium cromglycate, nedocromil sodium, phenyloin, zinc citrate, mexitil, dilantin, sodium hyaluronate, or polaxamer 188.

An example of a protocol for using this agent is as follows: The blood is stored at 4° C. until processing. The tubes are spun at 1000 rpm for ten minutes in a centrifuge with braking power set at zero. The tubes are spun a second time at 1000 rpm for ten minutes. The supernatant (the plasma) of each sample is transferred to a new tube and spun at 3000 rpm for ten minutes with the brake set at zero. The supernatant is transferred to a new tube and stored at −80° C. Approximately two milliliters of the “buffy coat,” which contains maternal cells, is placed into a separate tube and stored at −80° C.

Methods of the invention also involve preparing amplified cDNA from total RNA. cDNA is prepared and indiscriminately amplified without diluting the isolated RNA sample or distributing the mixture of genetic material in the isolated RNA into discrete reaction samples. Preferably, amplification is initiated at the 3′ end as well as randomly throughout the whole transcriptome in the sample to allow for amplification of both mRNA and non-polyadenylated transcripts. The double-stranded cDNA amplification products are thus optimized for the generation of sequencing libraries for Next Generation Sequencing platforms. Suitable kits for amplifying cDNA in accordance with the methods of the invention include, for example, the Ovation® RNA-Seq System.

Methods of the invention also involve sequencing the amplified cDNA. While any known sequencing method can be used to sequence the amplified cDNA mixture, single molecule sequencing methods are preferred. Preferably, the amplified cDNA is sequenced by whole transcriptome shotgun sequencing (also referred to herein as (“RNA-Seq”). Whole transcriptome shotgun sequencing (RNA-Seq) can be accomplished using a variety of next-generation sequencing platforms such as the Illumina Genome Analyzer platform, ABI Solid Sequencing platform, or Life Science's 454 Sequencing platform.

Methods of the invention further involve subjecting the cDNA to digital counting and analysis. The number of amplified sequences for each transcript in the amplified sample can be quantitated via sequence reads (one read per amplified strand). Unlike previous methods of digital analysis, sequencing allows for the detection and quantitation at the single nucleotide level for each transcript present in a biological sample containing a genetic material from different genomic sources and therefore multiple transcriptomes.

After digital counting, the ratios of the various amplified transcripts can compared to determine relative amounts of differential transcript in the biological sample. Where multiple biological samples are obtained at different time-points, the differential transcript levels can be characterized over the course of time.

Differential transcript levels within the biological sample can also be analyzed using via microarray techniques. The amplified cDNA can be used to probe a microarray containing gene transcripts associated with one or conditions or diseases, such as any prenatal condition, or any type of cancer, inflammatory, or autoimmune disease.

It will be understood that methods and any flow diagrams disclosed herein can be implemented by computer program instructions. These program instructions may be provided to a computer processor, such that the instructions, which execute on the processor, create means for implementing the actions specified in the flowchart blocks or described in methods for assessing tissue disclosed herein. The computer program instructions may be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer implemented process. The computer program instructions may also cause at least some of the operational steps to be performed in parallel. Moreover, some of the steps may also be performed across more than one processor, such as might arise in a multi-processor computer system. In addition, one or more processes may also be performed concurrently with other processes or even in a different sequence than illustrated without departing from the scope or spirit of the invention.

The computer program instructions can be, stored on any suitable computer-readable medium including, but not limited to, RAM. ROM. EEPROM, flash memory or other memory technology. CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computing device.

In certain aspects, methods of the invention can be used to determine cell-free RNA transcripts specific to the certain tissue, and use those transcripts to diagnose disorders and diseases associated with that tissue. In certain embodiments, methods of the invention can be used to determine cell-free RNA transcripts specific to the brain, and use those transcripts to diagnose neurological disorders (such as Alzheimer's disease). For example, methods of profiling cell-free RNA described herein can be used to differentiate subjects with neurological disorders from normal subjects because cell-free RNA transcripts associated with certain neurological disorders present at statistically-significant different levels than the same cell-free RNA transcripts in normal healthy populations. As a result, one is able to utilize levels of those RNA transcripts for clear and simple diagnostic tests.

In accordance with certain embodiments, cell-free RNA transcripts that source from brain tissue can be further examined as potential biomarkers for neurological disorders. In certain embodiments, once a brain-specific cell-free RNA transcript is determined, levels of the brain-specific cell-free RNA transcripts in normal patients are compared to patients with certain neurological disorders. In instances where the levels of brain specific cell-free RNA transcript consistently exhibit a statistically significant difference between subjects with a certain neurological disorder and normal subjects, then that brain-specific cell-free RNA transcript can be used as a biomarker for that neurological disorder. For example, the inventors have found that measurements of PSD3 and APP cell-free RNA transcript levels in plasma for Alzheimer disorder patients are statistically different from the levels of PSD3 and APP cell-free RNA in normal subjects.

According to certain aspects, a neurological disorder is indicated in a patient based on a comparison of the patient's circulating nucleic acid that is specific to brain tissue and circulating nucleic acid of a reference or multiple references that is specific to brain tissue. In particular, the circulating nucleic acid is RNA, but may also be DNA. In certain embodiments, levels of brain-specific circulating RNA present in a reference population are used as thresholds that are indicative with a condition. The condition may be a normal healthy condition or may be a diseased condition (e.g. neurological disorder, Alzheimer's disease generally or particular stage of Alzheimer's disease). When the threshold is indicative of a diseased condition, the patient's transcript levels that are underexpressed or overexpressed in comparison to the threshold may indicate that the patient does not have the disease. When the threshold is indicative of normal condition, the patient's transcript levels that are underexpressed or overexpressed in comparison to the threshold may indicate that the patient has the disease.

Reference RNA levels (e.g. levels of circulating RNA) may be obtained by statistically analyzing the brain-specific transcript levels of a defined patient population. The reference levels may pertain to a healthy patient population or a patient population with a particular neurological disorder. In further examples, the references levels may be tailored to a more specific patient population. For example, a reference level may correlate to a patient population of a certain age and/or correspond to a patient population exhibiting symptoms associated with a particular stage of a neurological disorder. Other factors for tailoring the patient population for reference levels may include sex, familial history, environmental exposure, and/or phenotypic traits.

Brain-specific genes or transcripts may be determined by deconvolving the cell-free transcriptome as described above and outlined in FIG. 7. Brain-specific genes or transcripts may also be determined by directly analyzing brain tissue. In addition, Tables 1 and 2, as listed in Example 4 below, provide genes whose expression profiles are unique to certain tissue types. Particularly, Tables 1 and 2 list brain-specific genes corresponding with hypothalamus as well as genes corresponding with the whole brain (e.g. most brain tissue), prefrontal cortex, thalamus, etc. In certain embodiments, brain-specific genes or transcripts include APP, PSD3. MOBP, MAG, SLC2A1, TCF7L2, CDH22, CNTF, and PAQR6.

The brain-specific transcripts used in methods of the invention may correspond to cell-free transcripts released from certain types of brain tissue. The types of brain tissue include the pituitary, hypothalamus, thalamus, corpus callosum, cerebrum, cerebral cortex, and combinations thereof. In particular embodiments, the brain-specific transcripts correspond with the hypothalamus. The hypothalamus is bounded by specialized brain regions that lack an effective blood/brain barrier, and thus transcripts released from the hypothalamus are likely to be introduced into blood or plasma.

FIG. 19 illustrates the difference in levels of PSD3 and APP cell-free RNA between subjects with Alzheimer's and normal subjects. Measurements of PSD3 and APP cell free RNA transcripts levels in plasma shows that the levels of these two transcripts are elevated in AD patients and can be used to cleanly group the AD patients from the normal patients. Shown in the figure are only two potential transcripts showing significant diagnostic potential. High throughput microfluidics chip allow for simultaneous measurements of other brain specific transcripts which can improve the classification process.

In particular aspects, brain-specific transcripts are used to characterize and diagnose neurological disorders. The neurological disorder characterized may include degenerative neurological disorders, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and some types of multiple sclerosis. The most common neurological disorder is Alzheimer's disease. In some instances, the neurological disorder is classified by the extent of cognitive impairment, which may include no impairment, mild impairment, moderate impairment, and severe impairment.

Alzheimer's disease is characterized into stages based on the cognitive symptoms that occur as the disease progresses. Stage 1 involves no impairment (normal function). The person does not experience any memory problems or signs of dementia. Stage 2 involves a very mild decline in cognitive functions. During Stage 2, a person may experience mild memory loss, but cognitive impairment is not likely noticeable by friends, family, and treating physicians. Stage 3 involves a mild cognitive decline, in which friends, family, and treating physicians may notice difficulties in the individual's memory and ability to perform tasks. For example, trouble identifying certain words, noticeable difficulty in performing tasks in social or work settings, forgetting just-read materials. Stage 4 involves moderate cognitive decline, which is noticeable and causes a significant impairment on the individual's daily life. In Stage 4, the individual will have trouble performing everyday complex tasks, such as managing financings and planning social gatherings, will have trouble remembering their own personal history, and becomes moody or withdrawn. Stage 5 involves moderately severe cognitive decline, in which gaps in memory and thinking are noticeable and the individual will begin to need help with certain activities. In Stage 5, individuals will be confused about the day, will have trouble with recalling particular details (such as phone number and street address), but will be able to remember significant details about themselves and their loved ones. Stage 6 involves severe cognitive decline, as the individual's memory continues to worsen. Individuals in Stage 6 will likely need extensive help with daily activities because they lose awareness of their surroundings and while they often remember certain tasks, they forget how to complete them or make mistakes (e.g. wearing pajamas during the day, forgetting to rinse after shampooing, wearing shoes on wrong side of the foot). Stage 7 involves very severe cognitive decline and is the final stage of Alzheimer's disease. In Stage 7, individuals lose their ability to respond to the environment, remember others, carry on a conversation, and control movement. Individuals need help with daily care, eating, dressing, using the bathroom, and have abnormal reflexes and tense muscles. Individuals may still be verbal, but will not make sense or relate to the present.

In certain embodiments, methods for assessing a neurological disorder involve a comparison of one or more brain-specific transcripts of an individual to a set of predictive variables correlated with the neurological disorder. The set of predictive variables may include a variety of reference levels that are brain specific. For instance, the set of predictive variables may include brain-specific transcript levels of a plurality of references. For example, one reference level may correspond to a normal patient population and another reference level may correspond to a patient population with the neurological disorder. In further examples, the references may correspond to more specific patient populations. For example, each reference level may correlate to a patient population of a certain age and/or correspond to a patient population exhibiting symptoms associated with a particular stage of a neurological disorder. Other factors for tailoring the patient population for reference levels may include sex, familial history, environmental exposure, and/or phenotypic traits.

Statistical analyses can be used to determine brain-specific reference levels of certain patient populations (such as those discussed above). Statistical analyses for identifying trends in patient populations and comparing patient populations are known in the art. Suitable statistical analyses include, but are not limited to, clustering analysis, principle component analysis, non-parametric statistical analyses (e.g. Wilcoxon tests), etc.

In addition, statistical analyses may be used to statistically significant deviations between the individual's circulating nucleic specific to brain tissue and that of a reference. When the reference is based on a diseased population, statistically significant deviations of the individual's brain-specific circulating RNA to those of the diseased population are indicative of no neurological disorder. When the reference is based on a normal population, statistically significant deviations of the individual's brain-specific circulating RNA to those of the normal population are indicative of a neurological disorder. Methods of determining statistical significance are known in the art. P-values and odds ratio can be used for statistical inference. Logistic regression models are common statistical classification models. In addition, Chi-Square tests and T-test may also be used to determine statistical significance.

Methods of the invention can also be used to identify one or more biomarkers associated with a neurological disorder. In such aspects, brain-specific transcripts of an individual or patient population suspected of having or actually having a neurological disorder (e.g. exhibiting impaired cognitive functions) are compared to reference brain-specific transcript (e.g. a healthy, normal control). The brain-specific transcripts of the individual or patient population that are differentially expressed as compared to the reference may then be identified as biomarkers of the neurological disorder. In certain embodiments, only differentially expressed brain-specific transcripts that are statistically significant are identified as biomarkers.

In certain embodiments, methods of the invention provide recommend a course of treatment based on the clinical indications determined by comparing of the patient's circulating brain-specific RNA and the reference. Depending on the diagnosis, the course of treatment may include medicinal therapy, behavioral therapy, sleep therapy, and combinations thereof. The course of treatment and diagnosis may be provided in a read-out or a report.

EXAMPLES Example 1: Profiling Maternal Plasma Cell-Free RNA by RNA Sequencing-A Comprehensive Approach Overview:

The plasma RNA profiles of 5 pregnant women were collected during the first trimester, second trimester, post-partum, as well as those of 2 non-pregnant female donors and 2 male donors using both microarray and RNA-Seq.

Among these pregnancies, there were 2 pregnancies with clinical complications such as premature birth and one pregnancy with bi-lobed placenta. Comparison of these pregnancies against normal cases reveals genes that exhibit significantly different gene expression pattern across different temporal stages of pregnancy. Application of such technique to samples associated with complicated pregnancies may help identify transcripts that can be used as molecular markers that are predictive of these pathologies.

Study Design and Methods: Subjects

Samples were collected from 5 pregnant women were during the first trimester, second trimester, third trimester, and post-partum. As a control, blood plasma samples were also collected from 2 non-pregnant female donors and 2 male donors.

Blood Collection and Processing

Blood samples were collected in EDTA tube and centrifuged at 1600 g for 10 min at 4′C. Supernatant were placed in 1 ml aliquots in a 1.5 ml microcentrifuge tube which were then centrifuged at 16000 g for 10 min at 4° C. to remove residual cells. Supernatants were then stored in 1.5 ml microcentrifuge tubes at −80° C. until use.

RNA Extraction and Amplification

The cell-free maternal plasma RNAs was extracted by Trizol LS reagent. The extracted and purified total RNA was converted to cDNA and amplified using the RNA-Seq Ovation Kit (NuGen). (The above steps were the same for both Microarray and RNA-Seq sample preparation).

The cDNA was fragmented using DNase I and labeled with Biotin, following by hybridization to Affymetrix GeneChip ST 1.0 microarrays. The Illumina sequencing platform and standard Illumina library preparation protocols were used for sequencing.

Data Analysis: Correlation Between Microarray and RNA-Seq

The RMA algorithm was applied to process the raw microarray data for background correction and normalization. RPKM values of the sequenced transcripts were obtained using the CASAVA 1.7 pipeline for RNA-seq. The RPKM in the RNA-Seq and the probe intensities in the microarray were converted to log 2 scale. For the RNA-Seq data, to avoid taking the log of 0, the gene expressions with RPKM of 0 were set to 0.01 prior to taking logs. Correlation coefficients between these two platforms ranges were then calculated.

Differential Expression of RNA Transcripts Levels Using RNA-Seq

Differential gene expression analysis was performed using edgeR, a set of library functions which are specifically written to analyze digital gene expression data. Gene Ontology was then performed using DAVID to identify for significantly enriched GO terms.

Principle Component Analysis & Identification of Significant Time Varying Genes

Principle component analysis was carried out using a custom script in R. To identify time varying genes, the time course library of functions in R were used to implement empirical Bayes methods for assessing differential expression in experiments involving time course which in our case are the different trimesters and post-partum for each individual patients.

Results and Discussion

RNA-Seq Reveals that Pregnancy-Associated Transcripts are Detected at Significantly Different Levels Between Pregnant and Non-Pregnant Subjects.

A comparison of the transcripts level derived using RNA-Seq and Gene Ontology Analysis between pregnant and non-pregnant subjects revealed that transcripts exhibiting differential transcript levels are significantly associated with female pregnancy, suggesting that RNA-Seq are enabling observation of real differences between these two class of transcriptome due to pregnancy. The top rank significantly expressed gene is PLAC4 which has also been known as a target in previous studies for developing RNA based test for trisomy 21. A listing of the top detected female pregnancy associated differentially expressed transcripts is shown in FIG. 1.

Principle Component Analysis (PCA) on Plasma Cell Free RNA Transcripts Levels in Maternal Plasma Distinguishes Between Pre-Mature and Normal Pregnancy

Using the plasma cell free transcript level profiles as inputs for Principle Component Analysis, the profile from each patient at different time points clustered into different pathological clusters suggesting that cell free plasma RNA transcript profile in maternal plasma may be used to distinguish between pre-term and non-preterm pregnancy.

Plasma Cell free RNA levels were quantified using both microarray and RNA-Seq. Transcripts expression levels profile from microarray and RNA-Seq from each patient are correlated with a Pearson correlation of approximately 0.7. Plots of the two main principal components for cell free RNA transcript levels is shown in FIG. 2.

Identification of Cell Free RNA Transcripts in Maternal Plasma Exhibiting Significantly Different Time Varying Trends Between Pre-Term and Normal Pregnancy Across all Three Trimesters and Post-Partum

A heatmap of the top 100 cell free transcript levels exhibiting different temporal levels in preterm and normal pregnancy using microarrays is shown in FIG. 3A. A heatmap of the top 100 cell free transcript levels exhibiting different temporal levels in preterm and normal pregnancy using RNA-Seq is shown in FIG. 3B.

Common Cell Free RNA Transcripts Identified by Microarray and RNA-Seq which Exhibit Significantly Different Time Varying Trends Between Pre-Term and Normal Pregnancy Across all Three Trimesters and Post-Partum

A ranking of the top 20 transcripts differentially expressed between pre-term and normal pregnancy is shown in FIG. 4. These top 20 common RNA transcripts were analyzed using Gene Ontology and were shown to be enriched for proteins that are attached (integrated or loosely bound) to the plasma membrane or on the membranes of the platelets (see FIG. 5).

Gene Expression Profiles for PVALB

The protein encoded by PVALB gene is a high affinity calcium ion-binding protein that is structurally and functionally similar to calmodulin and troponin C. The encoded protein is thought to be involved in muscle relaxation. As shown in FIG. 6, the gene expression profile for PVALB across the different trimesters shows the premature births [highlighted in blue] has higher levels of cell free RNA transcripts found as compared to normal pregnancy.

Conclusion:

Results from quantification and characterization of maternal plasma cell-free RNA using RNA-Seq strongly suggest that pregnancy associated transcripts can be detected.

Furthermore, both RNA-Seq and microarray methods can detect considerable gene transcripts whose level showed differential time trends that has a high probability of being associated with premature births.

The methods described herein can be modified to investigate pregnancies of different pathological situations and can also be modified to investigate temporal changes at more frequent time points.

Example 2: Quantification of Tissue-Specific Cell-Free RNA Exhibiting Temporal Variation During Pregnancy Overview:

Cell-free fetal DNA found in maternal plasma has been exploited extensively for non-invasive diagnostics. In contrast, cell-free fetal RNA which has been shown to be similarly detected in maternal circulation has yet been applied widely as a form of diagnostics. Both fetal cell-free RNA and DNA face similar challenges in distinguishing the fetal from maternal component because in both cases the maternal component dominates. To detect cell-free RNA of fetal origin, focus can be placed on genes that are highly expressed only during fetal development, which are subsequently inferred to be of fetal in origin and easily distinguished from background maternal RNA. Such a perspective is collaborated by studies that has established that cell-free fetal RNA derived from genes that are highly expressed in the placenta are detectable in maternal plasma during pregnancy.

A significant characteristic that set RNA apart from DNA can be attributed to RNA transcripts dynamic nature which is well reflected during fetal development. Life begins as a series of well-orchestrated events that starts with fertilization to form a single-cell zygote and ends with a multi-cellular organism with diverse tissue types. During pregnancy, majority of fetal tissues undergoes extensive remodeling and contain functionally diverse cell types. This underlying diversity can be generated as a result of differential gene expression from the same nuclear repertoire: where the quantity of RNA transcripts dictate that different cell types make different amount of proteins, despite their genomes being identical. The human genome comprises approximately 30,000 genes. Only a small set of genes are being transcribed to RNA within a particular differentiated cell type. These tissue specific RNA transcripts have been identified through many studies and databases involving developing fetuses of classical animal models. Combining known literature available with high throughput data generated from samples via sequencing, the entire collection of RNA transcripts contained within maternal plasma can be characterized.

Fetal organ formation during pregnancy depends on successive programs of gene expression. Temporal regulation of RNA quantity is necessary to generate this progression of cell differentiation events that accompany fetal organ genesis. To unravel similar temporal dynamics for cell free RNA, the expression profile of maternal plasma cell free RNA, especially the selected fetal tissue specific panel of genes, as a function across all three trimesters during pregnancy and post-partum were analyzed. Leveraging high throughput qPCR and sequencing technologies capability for simultaneous quantification of cell free fetal tissue specific RNA transcripts, a system level view of the spectrum of RNA transcripts with fetal origins in maternal plasma was obtained. In addition, maternal plasma was analyzed to deconvolute the heterogeneous cell free transcriptome of fetal origin a relative proportion of the different fetal tissue types. This approach incorporated physical constraints regarding the fetal contributions in maternal plasma, specifically the fraction of contribution of each fetal tissues were required to be non-negative and sum to one during all three trimesters of the pregnancy. These constraints on the data set enabled the results to be interpreted as relative proportions from different fetal organs. That is, a panel of previously selected fetal tissue-specific RNA transcripts exhibiting temporal variation can be used as a foundation for applying quadratic programming in order to determine the relative tissue-specific RNA contribution in one or more samples.

When considered individually, quantification of each of these fetal tissue specific transcripts within the maternal plasma can be used as a measure for the apoptotic rate of that particular fetal tissue during pregnancy. Normal fetal organ development is tightly regulated by cell division and apoptotic cell death. Developing tissues compete to survive and proliferate, and organ size is the result of a balance between cell proliferation and death. Due to the close association between aberrant cell death and developmental diseases, therapeutic modulation of apoptosis has become an area of intense research, but with this comes the demand for monitoring the apoptosis rate of specific. Quantification of fetal cell-free RNA transcripts provide such prognostic value, especially in premature births where the incidence of apoptosis in various organs of these preterm infants has been have been shown to contribute to neurodevelopmental deficits and cerebral palsy of preterm infants.

Sample Collection and Study Design

Selection of Fetal Tissue Specific Transcript Panel

To detect the presence of these fetal tissue-specific transcripts, a list of known fetal tissue specific genes was prepared from known literature and databases. The specificity for fetal tissues was validated by cross referencing between two main databases: TISGeD (Xiao, S.-J., Zhang, C. & Ji, Z.-L. TiSGeD: a Database for Tissue-Specific Genes. Bioinformatics (Oxford, England) 26, 1273-1275 (2010)) and BioGPS (Wu. C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome biology 10,R130 (2009); Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proceedings of the National Academy of Sciences of the United States of America 101, 6062-7 (2004)). Most of these selected transcripts are associated with known fetal developmental processes. This list of genes was overlapped with RNA sequencing and microarray data to generate the panel of selected fetal tissue-specific transcripts shown in FIG. 8.

Subjects

Samples of maternal blood were collected from normal pregnant women during the first trimester, second trimester, third trimester, and post-partum. For positive controls, fetal tissue specific RNA from the various fetal tissue types were bought from Agilent. Negative controls for the experiments were performed with the entire process with water, as well as with samples that did not undergoes the reverse transcription process.

Blood Collection and Processing

At each time-point, 7 to 15 mL of peripheral blood was drawn from each subject. Blood was centrifuged at 1600 g for 10 mins and transferred to microcentrifuge tubes for further centrifugation at 16000 g for 10 mins to remove residual cells. The above steps were carried out within 24 hours of the blood draw. Resulting plasma is stored at −80 Celsius for subsequent RNA extractions.

RNA Extraction

Cell free RNA extractions were carried using Trizol followed by Qiagen's RNeasy Mini Kit. To ensure that there are no contaminating DNA. DNase digestion is performed after RNA elution using RNase free DNase from Qiagen. Resulting cell free RNA from the pregnant subjects was then processed using standard microarrays and Illumina RNA-seq protocols. These steps generate the sequencing library that we used to generate RNA-seq data as well as the microarray expression data. The remaining cell free RNA are then used for parallel qPCR.

Parallel qPCR of Selected Transcripts

Accurate quantification of these fetal tissue specific transcripts was carried out using the Fluidigm BioMark system (See e.g. Spurgeon, S. L., Jones. R. C. & Ramakrishnan, R. High throughput gene expression measurement with real time PCR in a microfluidic dynamic array. PloS one 3, e1662 (2008)). This system allows for simultaneous query of a panel of fetal tissue specific transcripts. Two parallel forms of inquiry were conducted using different starting source of material. One was using the cDNA library from the Illumina sequencing protocol and the other uses the eluted RNA directly. Both sources of material were amplified with evagreen primers targeting the genes of interest. Both sources, RNA and cDNA, were preamplified. cDNA is preamplifed using evagreen PCR supermix and primers. RNA source is preamplified using the CellsDirect One-Step qRT-PCR kit from Invitrogen. Modifications were made to the default One-Step qRT-PCR protocol to accommodate a longer incubation time for reverse transcription. 19 cycles of preamplfication were conducted for both sources and the collected PCR products were cleaned up using Exonuclease I Treatment. To increase the dynamic range and the ability to quantify the efficiency of the later qPCR steps, serial dilutions were performed on the PCR products from 5 fold, 10 fold and 10 fold dilutions. Each of the collected maternal plasma from individual pregnant women across the time points went through the same procedures and was loaded onto 48×48 Dynamic Array Chips from Fluidigm to perform the qPCR. For positive control, fetal tissue specific RNA from the various fetal tissue types were bought from Agilent. Each of these RNA from fetal tissues went through the same preamplification and clean-up steps. A pool sample with equal proportions of different fetal tissues was created as well for later analysis to deconvolute the relative contribution of each tissue type in the pooled samples. All collected data from the Fluidigm BioMark system were pre-processed using Fluidigm Real Time PCR Analysis software to obtain the respective Ct values for each of the transcript across all samples. Negative controls of the experiments were performed with the entire process with water, as well as with samples that did not undergoes the reverse transcription process.

Data Analysis:

Fetal tissue specific RNA transcripts clear from the maternal peripheral bloodstream within a short period after birth. That is, the post-partum cell-free RNA transcriptome of maternal blood lacks fetal tissue specific RNA transcripts. As a result, it is expected that the quantity of these fetal tissue-specific transcripts to be higher before than after birth. The data of interest were the relative quantitative changes of the tissue specific transcripts across all three trimesters of pregnancy as compared to this baseline level after the baby is born. As described the methods, the fetal tissue-specific transcripts were quantified in parallel both using the actual cell-free RNA as well as the cDNA library of the same cell-free RNA. An example of the raw data obtained is shown in FIGS. 9A and 9B. The qPCR system gave a better quality readout using the cell-free RNA as the initial source. Focusing on the qPCR results from the direct cell-free RNA source, the analysis was conducted by comparing the fold changes level of each of these fetal tissue specific transcripts across all three trimesters using the post-partum level as the baseline for comparison. The Delta-Delta Ct method was employed (Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nature Protocols 3, 1101-1108 (2008)). Each of the transcript expression level was compared to the housekeeping genes to get the delta Ct value. Subsequently, to compare each trimesters to after birth, the delta-delta Ct method was applied using the post-partum data as the baseline.

Results and Discussion:

As shown in FIGS. 10, 11, and 12, the tissue-specific transcripts are generally found to be at a higher level during the trimesters as compared to after-birth. In particular, the tissue-specific panel of placental, fetal brain and fetal liver specific transcripts showed the same bias, where these transcripts are typically found to exist at higher levels during pregnancy then compared to after birth. Between the different trimesters, a general trend showed that the quantity of these transcripts increase with the progression into pregnancy.

Biological Significance of Quantified Fetal Tissue-Specific RNA:

Most of the transcripts in the panel were involved in fetal organ development and many are also found within the amniotic fluid. Once such example is ZNF238. This transcript is specific to fetal brain tissue and is known to be vital for cerebral cortex expansion during embryogenesis when neuronal layers are formed. Loss of ZNF238 in the central nervous system leads to severe disruption of neurogenesis, resulting in a striking postnatal small-brain phenotype. Using methods of the invention, one can determine whether ZNF238 is presenting in healthy, normal levels according to the stage of development.

Known defects due to the loss of ZNF238 include a striking postnatal small-brain phenotype: microcephaly, agenesis of the corpus callosum and cerebellar hypoplasia. Microcephaly can sometimes be diagnosed before birth by prenatal ultrasound. In many cases, however, it might not be evident by ultrasound until the third trimester. Typically, diagnosis is not made until birth or later in infancy upon finding that the baby's head circumference is much smaller than normal. Microcephaly is a life-long condition and currently untreatable. A child born with microcephaly will require frequent examinations and diagnostic testing by a doctor to monitor the development of the head as he or she grows. Early detection of ZNf238 differential expression using methods of the invention provides for prenatal diagnosis and may hold prognostic value for drug treatments and dosing during course of treatment.

Beyond ZNF238, many of the characterized transcripts may hold diagnostic value in developmental diseases involving apoptosis, i.e., diseases caused by removal of unnecessary neurons during neural development. Seeing that apoptosis of neurons is essential during development, one could extrapolate that similar apoptosis might be activated in neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and amyotrophic lateral sclerosis. In such a scenario, the methodology described herein will allow for close monitoring for disease progression and possibly an ideal dosage according to the progression.

Deducing Relative Contributions of Different Fetal Tissue Types:

Differential rate of apoptosis of specific tissues may directly correlate with certain developmental diseases. That is, certain developmental diseases may increase the levels of a particular specific RNA transcripts being observed in the maternal transcriptome. Knowledge of the relative contribution from various tissue types will allow for observations of these types of changes during the progression of these diseases. The quantified panel of fetal tissue specific transcripts during pregnancy can be considered as a summation of the contributions from the various fetal tissues (See FIG. 25).

Expressing,

$Y_{i} = {{\sum\limits_{j}{\pi_{i}x_{ij}}} + ɛ}$

where Y is the observed transcript quantity in maternal plasma for gene i, X is the known transcript quantity for gene i in known fetal tissue j and ε the normally distributed error. Additional physical constraints includes:

-   -   1. Summation of all fraction contributing to the observed         quantification is 1, given by the condition: Σπ_(i)=1     -   2. All the contribution from each tissue type has to greater         than or equal zero. There is no physical meaning to having a         negative contribution. This is given by π_(i)≥0, since n is         defined as the fractional contribution of each tissue types.

Consequently to obtain the optimal fractional contribution of each tissue type, the least-square error is minimized. The above equations are then solved using quadratic programming in R to obtain the optimal relative contributions of the tissue types towards the maternal cell free RNA transcripts. In the workflow, the quantity of RNA transcripts are given relative to the housekeeping genes in terms of Ct values obtained from qPCR. Therefore, the Ct value can be considered as a proxy of the measured transcript quantity. An increase in Ct value of one is similar to a two-fold change in transcript quantity. i.e. 2 raised to the power of 1. The process beings with normalizing all of the data in CT relative to the housekeeping gene, and is followed by quadratic programming.

As a proof of concept for the above scheme, different fetal tissue types (Brain, Placenta, Liver, Thymus, Lung) were mixed in equal proportions to generate a pool sample. Each fetal tissue types (Brain, Placenta, Liver, Thymus, Lung) along with the pooled sample were quantified using the same Fluidigm Biomark System to obtain the Ct values from qPCR for each fetal tissue specific transcript across all tissues and the pooled sample. These values were used to perform the same deconvolution. The resulting fetal fraction of each of the fetal tissue organs (Brain, Placenta, Liver, Thymus, Lung) was 0.109, 0.206, 0.236, 0.202 & 0.245 respectively.

Conclusion:

In summary, the panel of fetal specific cell free transcripts provides valuable biological information across different fetal tissues at once. Most particularly, the method can deduce the different relative proportions of fetal tissue-specific transcripts to total RNA, and, when considered individually, each transcript can be indicative of the apoptotic rate of the fetal tissue. Such measurements have numerous potential applications for developmental and fetal medicine. Most human fetal development studies have relied mainly on postnatal tissue specimens or aborted fetuses. Methods described herein provide quick and rapid assay of the rate of fetal tissue/organ growth or death on live fetuses with minimal risk to the pregnant mother and fetus. Similar methods may be employed to monitor major adult organ tissue systems that exhibit specific cell free RNA transcripts in the plasma.

Example 3: Additional Study for Quantification of Tissue-Specific Cell-Free RNA Exhibiting Temporal Variation During Pregnancy

High-throughput methods of microarray and next-generation sequencing were used to characterize the landscape of cell-free RNA transcriptome of healthy adults and of pregnant women across all three trimesters of pregnancy and post-partum. The results confirm the study presented in Example 2, by showing that it is possible to monitor the gene expression status of many tissues and the temporal expression of certain genes can be measured across the stages of human development. The study also investigated the role of cell-free RNA in adult's suffering from neurodegenerative disorder Alzheimer's and observed a marked increase of neuron-specific transcripts in the blood of affected individuals. Thus, this study shows that the same principles of observing tissue-specific RNA to assess development can also be applied to assess the deterioration of brain tissue associated with neurological disorders.

Overview

An additional study following the guidance of Example 2 was conducted to illustrate the temporal variation among tissue-specific cell-free RNA across trimesters. FIG. 18 outlines the experimental design for this study, which examined cell-free plasma samples of 15 subjects, of which 11 were pregnant and 4 were not pregnant (2 males; 2 females). The blood samples were taken over several time-points: 1st, 2nd, and 3rd Trimester and Post-Partum. The cell-free plasma RNA were then extracted, amplified, and characterized by Affymetrix microarray, Illumina Sequencer, and quantitative PCR. For each plasma sample. ˜20 million sequencing reads were generated, ˜80% of which could be mapped against the human reference genome (hg 19). As the plasma RNA is of low concentration and vulnerable to degradation, contamination from the plasma DNA is a concern. To assess the quality of the sequencing library, the number of reads assigned to different regions was counted: 34% mapped to exons, 18% mapped to introns, and 24% mapped to ribosomal RNA and tRNA. Therefore, dominant portion of the reads originated from RNA transcripts rather than DNA contamination. To validate the RNA-seq measurements, all of the plasma samples were also analyzed with gene expression microarrays.

Apoptotic cells from different tissue types release their RNA into the cell-free RNA component in plasma. Each of these tissues expresses a number of genes unique to their tissue type, and the observed cell-free RNA transcriptomes can be considered as a summation of contributions from these different tissue types. Using expression data of different tissue types available in public databases, the cell-free RNA transcriptome from our four nonpregnant subjects were deconvoluted using quadratic programming to reveal the relative contributions of different tissue types (FIG. 26). These contributions identified different tissue types which are consistent among different control subjects. Whole blood, as expected, is the major contributor (˜40%) toward the cell-free RNA transcriptome. Other major contributing tissue types include the bone marrow and lymph nodes. One also sees consistent contributions from smooth muscle, epithelial cells, thymus, and hypothalamus.

Results and Discussion

Within the cohort, about 100 genes were analyzed whose RNA transcripts contained paternal SNPs that were distinct from the maternal inheritance to explicitly demonstrate that the fetus contributes a substantial amount of RNA to the mother's blood (See FIG. 21). To accurately quantify and verify the relative fetal contribution, the following were genotyped: a mother and her fetus and inferred paternal genotype. The weighted average fraction of fetal-originated cell-free RNA was quantified using paternal SNPs. Cell-free RNA fetal fraction depends on gene expression and varies greatly across different genes. In general, the fetal fraction of cell-free RNA increases as the pregnancy progress and decreases after delivery. The weighted average fetal fraction started at 0.4% in the first trimester, increased to 3.4% in the second trimester, and peaked at 15.4% in the third trimester. Although fetal RNA should be cleared after delivery, there was still 0.3% of fetal RNA as calculated, which can be attributed to background noise arising from misalignment and sequencing errors.

In addition to monitoring fetal tissue-specific mRNA, noncoding transcripts present in the cell-free compartment across pregnancy were identified. These noncoding transcripts include long noncoding RNAs (IncRNAs), as well as circular RNAs (circRNA). Additional PCR assays were designed to specifically amplify and validate the presence of these circRNA in plasma, circRNAs have recently been shown to be widely expressed in human cells and have greater stability than their linear counterparts, potentially making them reliable biomarkers for capturing transient events. Several of the circRNA species appear to be specifically expressed during different trimesters of pregnancy. The identification of these cell-free noncoding RNAs during pregnancy improve our ability to monitor the health of the mother and fetus.

There is a general increase in the number of genes detected across the different trimesters followed by a steep drop after the pregnancy. Such an increase in the number of genes detected suggests that unique transcripts are expressed specifically during particular time intervals in the developing fetus. FIGS. 18 and 19 show the heatmap of genes whose level changed over time during pregnancy, as detected by microarray. ANOVA was applied to identify genes that varied in expression in a statistically significant manner across different trimesters. An additional condition filtering for transcripts that were expressed at low levels in both the postpartum plasma of pregnant subjects and in nonpregnant controls. Using these conditions, 39 genes from RNA-seq and 34 genes from microarray were identified, of which there were 17 genes in common. Gene Ontology (GO) performed on the identified genes using Database for Annotation. Visualization and Integrated Discovery (DAVID) revealed that the identified gene list is enriched for the following GO terms: female pregnancy (Bonferroni-corrected P=5.5×10⁻⁵), extracellular region (corrected P=6.6×10⁻³), and hormone activity (corrected P=6.3×10⁻⁹). These RNA transcripts show a general trend of having low expression postpartum and the highest expression during the third trimester. Most of these transcripts are specifically expressed in the placenta, and their levels reach a maximum in the later stages of pregnancy.

Other nonplacental transcripts that share similar temporal trends. Two such significant transcripts were RAB6B and MARCH2, which are known to be expressed specifically in CD71+ erythrocytes. Erythrocytes enriched for CD71+ have been shown to contain fetal hemoglobin and are interpreted to be of fetal origin. The presence of transcripts with known specificity to different fetal tissue types reflects the fact that the cell-free transcriptome during the period of pregnancy can be considered as a summation of transcriptomes from various different fetal tissues on top of a maternal background.

This analysis detected the presence of numerous transcripts that are specifically expressed in several other fetal tissues, although the available sequencing depth resulted in limited concordance between samples. To verify the presence of these and other potential fetal tissue-specific transcripts, a panel of fetal tissue-specific transcripts was devised for detailed quantification using the more sensitive method of quantitative PCR (qPCR). Three main sources were focused on, which are of interest to fetal neurodevelopment and metabolism: placenta, fetal brain, and fetal liver. In FIGS. 22-24, the levels of these groups of fetal tissue-specific transcripts at different trimesters were systematically compared to the level seen in maternal serum after delivery. To illustrate the temporal trends, housekeeping genes as the baseline were used as a baseline, and ΔCt analysis was applied to find the level of relative expression these fetal tissue-specific transcripts with respect to the housekeeping genes. Many of these tissue-specific transcripts expressed at substantially higher levels during the pregnancy compared with postpartum. There was a general trend of an increase in the quantity of these transcripts across advancing gestation.

The placental qPCR assay focused on genes that are known to be highly expressed in the placenta, many of which encode for proteins that have been shown to be present in the maternal blood. The serum levels of these proteins are known to be involved in pregnancy complications such as preeclampsia and premature births. Examples in our panel includes ADAM12, which encodes for disintegrin, and metalloproteinase domain-containing protein 12. These proteinases are highly expressed in human placenta and are present at high concentrations in maternal serum as early as the first trimester. ADAM12 serum concentrations are known to be significantly reduced in pregnancies complicated by fetal trisomy 18 and trisomy 21 and may therefore be of potential use in conjunction with cell-free DNA for the detection of chromosomal abnormalities. Similarly, placental alkaline phosphatase, encoded by the ALPP gene, is a tissue-specific isoform expressed increasingly throughout pregnancy until term in the placenta. It is anchored to the plasma membrane of the syncytiotrophoblast and to a lesser extent of cytotrophoblastic cells. This enzyme is also released into maternal serum, and variations of its concentration are related with several clinical disorders such as preterm delivery. Another gene in the panel. BACE2, encoded the β site APP-cleaving enzyme, which generates amyloid-β protein by endoproteolytic processing. Brain deposition of amyloid-β protein is a frequent complication of Down syndrome patients, and BACE-2 is known to be overexpressed in Down syndrome.

Other transcripts in our placental assay are known to be transcribed at high levels in the placenta, and levels of these mRNAs are important for normal placental function and development in pregnancy. TAC3 is mainly expressed in the placenta and is significantly elevated in preeclamptic human placentas at term. Similarly, PLAC1 is essential for normal placental development. PLAC1 deficiency results in a hyperplastic placenta, characterized by an enlarged and dysmorphic junctional zone. An increase in cell-free mRNA of PLAC1 has been suggested to be correlated with the occurrence of preeclampsia.

On the fetal liver tissue-specific panel, one of the characterized transcripts is AFP. AFP encodes for α-fetoprotein and is transcribed mainly in the fetal liver. AFP is the most abundant plasma protein found in the human fetus. Clinically, AFP protein levels are measured in pregnant women in either maternal blood or amniotic fluid and serve as a screening marker for fetal aneuploidy, as well as neural tube and abdominal wall defects. Other fetal liver-specific transcripts that were characterized are highly involved in metabolism. An example is fetal liver-specific monooxygenase CYP3A7, which catalyzes many reactions involved in synthesis of cholesterol and steroids and is responsible for the metabolism of more than 50% of all clinical pharmaceuticals. In drug-treated diabetic pregnancies in which glucose levels in the woman are uncontrolled, neural tube and cardiac defects in the early developing brain, spine, and heart depend on functional GLUT2 carriers, whose transcripts are well characterized in the panel. Mutations in this gene results in Fanconi-Bickel syndrome, a congenital defect of facilitative glucose transport. Monitoring of fetal liver-specific transcripts during the drug regime may enable analysis of the fetuses' response to drug therapy that the mother is undergoing.

Example 4: Deconvolution of Adult Cell-Free Transcriptome Overview:

The plasma RNA profiles of 4 healthy, normal adults were analyzed. Based on the gene expression profile of different tissue types, the methods described quantify the relative contributions of each tissue type towards the cell-free RNA component in a donor's plasma. For quantification, apoptotic cells from different tissue types are assumed to release their RNA into the plasma. Each of these tissues expressed a specific number of genes unique to the tissue type, and the observed cell-free RNA transcriptome is a summation of these different tissue types.

Study Design and Methods:

To determine the contribution of tissue-specific transcripts to the cell-free adult transriptome, a list of known tissue-specific genes was prepared from known literature and databases. Two database sources were utilized: Human U133A/GNF1H Gene Atlas and RNA-Seq Atlas. Using the raw data from these two database, tissue-specific genes were identified by the following method. A template-matching process was applied to data obtained from the two databases for the purpose of identifying tissue-specific gene. The list of tissue specific genes identified by the method is provided in Table 1 below. The specificity and sensitivity of the panel is constrained by the number of tissue samples in the database. For example, the Human U133A/GNF1H Gene Atlas dataset includes 84 different tissue samples, and a panel's specificity from that database is constrained by the 84 sample sets. Similarly, for the RNA-seq atlas, there are 11 different tissue samples and specificity is limited to distinguishing between these 11 tissues. After obtaining a list of tissue-specific transcripts from the two databases, the specificity of these transcripts was verified with literature as well as the TisGED database.

The adult cell-free transcriptome can be considered as a summation of the tissue-specific transcripts obtained from the two databases. To quantitatively deduce the relative proportions of the different tissues in an adult cell-free transcriptome, quadratic programming is performed as a constrained optimization method to deduce the relative optimal contributions of different organs/tissues towards the cell free-transcriptome. The specificity and accuracy of this process is dependent on the table of genes (Table 2 below) and the extent by which that they are detectable in RNA-seq and microarray.

Subjects: Plasma samples were collected from 4 healthy, normal adults.

Initial Results:

Deconvolution of our adult cell-free RNA transcriptome from microarray using the above methods revealed the relative contributions of the different tissue and organs are tabulated in FIG. 13.

FIG. 13 shows that the normal cell free transcriptome for adults is consistent across all 4 subjects. The relative contributions between the 4 subjects do not differ greatly, suggesting that the relative contributions from different tissue types are relatively stable between normal adults. Out of the 84 tissue types available, the deduced optimal major contributing tissues are from whole blood and bone marrow.

An interesting tissue type contributing to circulating RNA is the hypothalamus. The hypothalamus is bounded by specialized brain regions that lack an effective blood-brain barrier; the capillary endothelium at these sites is fenestrated to allow free passage of even large proteins and other molecules which in our case we believed that RNA transcripts from apoptotic cells in that region could be released into the plasma cell free RNA component.

The same methods were performed on the subjects using RNA-seq. The results described herein are, limited due to the amount of tissue-specific RNA-Seq data available. However, it is understood that tissue-specific data is expanding with the increasing rate of sequencing of various tissue rates, and future analysis will be able to leverage those datasets. For RNA-seq data (as compared to microarray), whole blood nor the bone marrow samples are not available. The cell free transcriptome can only be decomposed to the available 11 different tissue types of RNA-seq data. Of which, only relative contributions from the hypothalamus and spleen were observed, as shown in FIG. 14.

A list of 84 tissue-specific genes (as provided in Table 2) was further selected for verification with qPCR. The Fluidigm BioMark Platform was used to perform the qPCR on RNA derived from the following tissues: Brain, Cerebellum, Heart, Kidney, Liver and Skin. Similar qPCR workflow was applied to the cell free RNA component as well. The delta Ct values by comparing with the housekeeping genes: ACTB was plotted in the heatmap format in FIG. 15, which shows that these tissue specific transcripts are detectable in the cell free RNA.

Tables for Example 4

The following table lists the tissue-specific genes for Example 4 that was obtained using raw data from the Human U33A/GNF1H Gene Atlas and RNA-Seq Atlas databases.

TABLE 1 List of Tissue-Specific Genes Determined by Deconvolution of Adult Transcriptome Gene Tissue A4GALT Uterus Corpus A4GNT Superior Cervical Ganglion AADAC small intestine AASS Ovary ABCA12 Tonsil ABCA4 retina ABCB4 CD19 Bcells neg. sel. ABCB6 CD71 Early Erythroid ABCB7 CD71 Early Erythroid ABCC2 Pancreatic Islet ABCC3 Adrenal Cortex ABCC9 Dorsal Root Ganglion ABCF3 Adrenal gland ABCG1 Lung ABCG2 CD71 Early Erythroid ABHD4 Adipocyte ABHD5 Whole Blood ABHD6 pineal night ABHD8 Whole Brain ABO Heart ABT1 X721 B lymphoblasts ABTB2 Placenta ACAA1 Liver ACACB Adipocyte ACAD8 Kidney ACADL Thyroid ACADS Liver ACADSB Fetal liver ACAN Trachea ACBD4 Liver ACCN3 Prefrontal Cortex ACE2 Testis Germ Cell ACHE CD71 Early Erythroid ACLY Adipocyte ACOT1 Adipocyte ACOX2 Liver ACP2 Liver ACP5 Lung ACP6 CD34 ACPP Prostate ACR Testis Intersitial ACRV1 Testis Intersitial ACSBG2 Testis Intersitial ACSF2 Kidney ACSL4 Fetal liver ACSL5 small intestine ACSL6 GD71 Early Erythroid ACSM3 Leukemia chronic Myelogenous K562 ACSM5 Liver ACSS3 Adipocyte ACTA1 Skeletal Muscle ACTC1 Heart ACTG1 CD71 Early Erythroid ACTL7A Testis Intersitial ACTL7B Testis Intersitial ACTN3 Skeletal Muscle ACTR8 Superior Cervical Ganglion ADA Leukemia lymphoblastic MOLT 4 ADAM12 Placenta ADAM17 CD33 Myeloid ADAM2 Testis Intersitial ADAM21 Appendix ADAM23 Thalamus ADAM28 CD19 Bcells neg. sel. ADAM30 Testis Germ Cell ADAM5P Testis Intersitial ADAM7 Testis Leydig Cell ADAMTS12 Atrioventricular Node ADAMTS20 Appendix ADAMTS3 CD105 Endothelial ADAMTS8 Lung ADAMTS9 Dorsal Root Ganglion ADAMTSL2 Ciliary Ganglion ADAMTSL3 retina ADAMTSL4 Atrioventricular Node ADARB2 Skeletal Muscle ADAT1 CD71 Early Erythroid ADCK4 Ciliary Ganglion ADCY1 Fetal brain ADCY9 Lung ADCYAP1 Pancreatic Islet ADH7 Tongue ADIPOR1 Bone marrow ADM2 Pituitary ADORA3 Olfactory Bulb ADRA1D Skeletal Muscle ADRA2A Lymph node ADRA2B Superior Cervical Ganglion ADRB1 pineal night AFF3 Trigeminal Ganglion AFF4 Testis Intersitial AGPAT2 Adipocyte AGPAT3 CD33 Myeloid AGPAT4 CD71 Early Erythroid AGPS Testis Intersitial AGR2 Trachea AGRN Colorectal adenocarcinoma AGRP Superior Cervical Ganglion AGXT Liver AIFM1 X721 B lymphoblasts AIM2 CD19 Bcells neg. sel. AJAP1 BDCA4 Dentritic Cells AKAP10 CD33 Myeloid AKAP3 Testis Intersitial AKAP6 Medulla Oblongata AKAP7 Fetal brain AKAP8L CD71 Early Erythroid AKR1C4 Liver AKR7A3 Liver AKT2 Thyroid ALAD CD71 Early Erythroid ALDH3B2 Tongue ALDH6A1 Kidney ALDH7A1 Ovary ALDOA Skeletal Muscle ALG12 CD4 T cells ALG13 CD19 Bcells neg. sel. ALG3 Liver ALOX12 Whole Blood ALOX12B Tonsil ALOX15B Prostate ALPI small intestine ALPK3 Skeletal Muscle ALPL Whole Blood ALPP Placenta ALPPL2 Placenta ALX1 Superior Cervical Ganglion ALX4 Superior Cervical Ganglion AMBN pineal day AMDHD2 BDCA4 Dentritic Cells AMELY Subthalamic Nucleus AMHR2 Heart AMPD1 Skeletal Muscle AMPD2 pineal night AMPD3 CD71 Early Erythroid ANAPC1 X721 B lymphoblasts ANG Liver ANGEL2 CD8 T cells ANGPT1 CD35 ANGPT2 Ciliary Ganglion ANGPTL2 Uterus Corpus ANGPTL3 Fetal liver ANK1 CD71 Early Erythroid ANKFY1 CD8 T cells ANKH Cerebellum Peduncles ANKLE2 Testis ANKRD1 Skeletal Muscle ANKRD2 Skeletal Muscle ANKRD34C Thalamus ANKRD5 Skeletal Muscle ANKRD53 Skeletal Muscle ANKRD57 Bronchial Epithelial Cells ANKS1B Superior Cervical Ganglion ANTXR1 Uterus Corpus ANXA13 small intestine ANXA2P1 Bronchial Epithelial Cells ANXA2P3 Bronchial Epithelial Cells AOC2 retina AP1G1 Testis Germ Cell AP1M2 Kidney AP351 Heart APBA1 Dorsal Root Ganglion APBB1IP Whole Blood APBB2 Superior Cervical Ganglion APC Fetal brain APEX2 Colorectal adenocarcinoma APIP Trachea APOA1 Liver APOA4 small intestine APOB48R Whole Blood APOBEC1 small intestine APOBEC2 Skeletal Muscle APOBEC3B Colorectal adenocarcinoma APOC4 Liver APOF Liver APOL5 Bone marrow APOOL Superior Cervical Ganglion AQP2 Kidney AQP5 Testis Intersitial AQP7 Adioocyte AR Liver ARCN1 Trigeminal Ganglion ARFGAP1 Lymphoma burkitts Raji ARG1 Fetal liver ARHGAP11A Trigeminal Ganglion ARHGAP19 Olfactory Bulb ARHGAP22 CD36 ARHGAP28 Testis Intersitial ARHGAP6 Prostate ARHGEF1 CD4 T cells ARHGEF5 Pancreas ARHGEF7 Thymus ARID3A Placenta ARID3B X721 B lymphoblasts ARL15 Uterus Corpus ARMC4 Superior Cervical Ganglion ARMC8 CD71 Early Erythroid ARMCX5 small intestine ARR3 retina ARSA Liver ARSB Superior Cervical Ganglion ARSE Liver ARSF Globus Pallidus ART1 Cardiac Myocytes ART3 Testis ART4 CD71 Early Erythroid ASB1 Trigeminal Ganglion ASB7 Globus Pallidus A5B8 Superior Cervical Ganglion ASCC2 CD71 Early Erythroid ASCL2 Superior Cervical Ganglion ASCL3 Superior Cervical Ganglion ASF1A CD71 Early Erythroid ASIP BDCA4 Dentritic Cells ASL Liver ASPN Uterus ASPSCR1 Colorectal adenocarcinoma ASTE1 CD8 T cells ASTN2 pineal day ATF5 Liver ATG4A CD71 Early Erythroid ATG7 CD14 Monocytes ATN1 Prefrontal Cortex ATOH1 Superior Cervical Ganglion ATP10A CD56 NK Cells ATP10D Placenta ATP11A Superior Cervical Ganglion ATP12A Trachea ATP13A3 Smooth Muscle ATP1B3 Adrenal Cortex ATP2C2 Colon ATP4A Adrenal gland ATP4B Parietal Lobe ATP5G1 Heart ATP5G3 Heart ATP5J2 Superior Cervical Ganglion ATP6V0A2 CD37 ATP6V1B1 Kidney ATP7A CD71 Early Erythroid ATRIP CD14 Monocytes ATXN3L Superior Cervical Ganglion ATXN7L1 Skeletal Muscle AURKC Testis Seminiferous Tubule AVEN Bronchial Epithelial Cells AVIL Dorsal Root Ganglion AVP Hypothalamus AXIN1 CD56 NK Cells AXL Cardiac Myocytes AZI1 CD71 Early Erythroid B3GALNT1 Amygdala B3GALT5 CD105 Endothelial B3GNT2 CD71 Early Erythroid B3GNT3 Placenta B3GNTL1 CD38 BAAT Liver BACH2 Lymphoma burkitts Daudi BAD Whole Brain BAG2 Uterus BAG4 Superior Cervical Ganglion BAI1 Cingulate Cortex BAIAP2 Liver BAIAP2L2 Superior Cervical Ganglion BAMBI Colorectal adenocarcinoma BANK1 CD19 Bcells neg. sel. BARD1 X721 B lymphoblasts BARX1 Atrioventricular Node BATF3 X721 B lymphoblasts BBOX1 Kidney BBS4 pineal day BCAM Thyroid BCAR3 Placenta BCAS3 X721 B lymphoblasts BCKDK Liver BCL10 Colon BCL2L1 CD71 Early Erythroid BCL2L10 Trigeminal Ganglion BCL2L13 pineal day BCL2L14 Testis BCL3 Whole Blood BDH1 Liver BDKRB1 Smooth Muscle BDKRB2 Smooth Muscle BDNF Smooth Muscle BECN1 Ciliary Ganglion BEST1 retina BET1L Superior Cervical Ganglion BHLHB9 pineal night BIRC3 CD19 Bcells neg. sel. BLK CD19 Bcells neg. sel. BLVRA CD105 Endothelial BMP1 Placenta BMP2K CD71 Early Erythroid BMP3 Temporal Lobe BMP5 Trigeminal Ganglion BMP8A Fetal Thyroid BMP8B Superior Cervical Ganglion BMPR1B Skeletal Muscle BNC1 Bronchial Epithelial Cells BNC2 Uterus BNIP3L CD71 Early Erythroid BOK Thalamus BPHL Kidney BPI Bone marrow BPY2 Adrenal gland BRAF Superior Cervical Ganglion BRAP Testis Intersitial BRE Adrenal gland BRS3 Skeletal Muscle BRSK2 Cerebellum Peduncles BSDC1 CD71 Early Erythroid BTBD2 Prefrontal Cortex BTD Superior Cervical Ganglion BTN2A3 Appendix BTN3A1 CD8 T cells BTRC CD71 Early Erythroid BUB1 X721 B lymphoblasts BYSL Leukemia chronic Myelogenous K563 C10orf118 Testis Leydig Cell C10orf119 CD33 Myeloid C10orf28 Superior Cervical Ganglion C10orf57 Ciliary Ganglion C10orf72 Adrenal Cortex C10orf76 CD19 Bcells neg. sel. C10orf81 Dorsal Root Ganglion C10orf84 Superior Cervical Ganglion C10orf88 Testis Seminiferous Tubule C10orf95 Superior Cervical Ganglion C11orf41 Fetal brain C11orf48 Adipocyte C11orf57 Appendix C11orf67 Skeletal Muscle C11orf71 Thyroid C11orf80 Leukemia lymphoblastic MOLT 5 C12orf4 CD71 Early Erythroid C12orf43 Whole Brain C12orf47 CD8 T cells C12orf49 CD56 NK Cells C13orf23 Placenta C13orf27 Testis Leydig Cell C13orf34 CD71 Early Erythroid C14orf106 CD33 Myeloid C14orf118 Superior Cervical Ganglion C14orf138 CD19 Bcells neg. sel. C14orf162 Cerebellum C14orf169 Testis C14orf56 Superior Cervical Ganglion C15orf2 Cerebellum C15orf29 Fetal brain C15orf39 Whole Blood C15orf44 Testis C15orf5 Superior Cervical Ganglion C16orf3 Dorsal Root Ganglion C16orf53 pineal day C16orf59 CD71 Early Erythroid C16orf68 Testis C16orf71 Testis Seminiferous Tubule C17orf42 X721 B lymphoblasts C17orf53 Dorsal Root Ganglion C17orf59 Dorsal Root Ganglion C17orf68 CD8 T cells C17orf73 Cardiac Myocytes C17orf80 Testis Germ Cell C17orf81 Testis Intersitial C17orf85 BDCA4 Dentritic Cells C17orf88 Superior Cervical Ganglion C19orf29 Leukemia chronic Myelogenous K564 C19orf61 Leukemia lymphoblastic MOLT 6 C1GALT1C1 Superior Cervical Ganglion C1orf103 Leukemia chronic Myelogenous K565 C1orf105 Testis Intersitial C1orf106 small intestine C1orf114 Testis Intersitial C1orf135 Testis C1orf14 Testis Leydig Cell C1orf156 CD19 Bcells neg. sel. C1orf175 Testis Intersitial C1orf222 Testis C1orf25 CD71 Early Erythroid C1orf27 pineal night C1orf35 CD71 Early Erythroid C1orf50 Testis C1orf66 Leukemia chronic Myelogenous K566 C1orf68 Liver C1orf89 Atrioventricular Node C1orf9 CD71 Early Erythroid C1QTNF1 Smooth Muscle C1QTNF3 Spinal Cord C2 Liver C20orf191 Superior Cervical Ganglion C20orf29 Superior Cervical Ganglion C21orf45 CD105 Endothelial C21orf7 Whole Blood C21orf91 Testis Intersitial C22orf24 Superior Cervical Ganglion C22orf26 Ciliary Ganglion C22orf30 Trigeminal Ganglion C22orf31 Uterus Corpus C2CD2 Adrenal Cortex C2orf18 Cerebellum C2orf34 pineal day C2orf42 Testis C2orf43 X721 B lymphoblasts C2orf54 Trigeminal Ganglion C3AR1 CD14 Monocytes C3orf37 Lymphoma burkitts Daudi C3orf64 pineal day C4orf19 Placenta C4orf23 Superior Cervical Ganglion C4orf6 Superior Cervical Ganglion C5 Fetal liver C5AR1 Whole Blood C5orf23 CD39 C5orf28 Thyroid C5orf4 CD71 Early Erythroid C5orf42 Superior Cervical Ganglion C6orf103 Testis Intersitial C6orf105 Colon C6orf108 Lymphoma burkitts Raji C6orf124 Fetal brain C6orf162 Pituitary C6orf208 Superior Cervical Ganglion C6orf25 Superior Cervical Ganglion C6orf27 Superior Cervical Ganglion C6orf35 Appendix C6orf54 Skeletal Muscle C6orf64 Testis C7orf10 Bronchial Epithelial Cells C7orf25 Superior Cervical Ganglion C7orf58 Leukemia chronic Myelogenous K567 C8G Liver C8orf17 Superior Cervical Ganglion C8orf41 Leukemia lymphoblastic MOLT 7 C9 Liver C9orf116 Testis C9orf27 Trigeminal Ganglion C9orf3 Uterus C9orf38 Superior Cervical Ganglion C9orf40 CD71 Early Erythroid C9orf46 Bronchial Epithelial Cells C9orf68 Skeletal Muscle C9orf86 CD71 Early Erythroid C9orf9 Testis Intersitial CA1 CD71 Early Erythroid CA12 Kidney CA3 Thyroid CA4 Lung CA5A Liver CA5B Superior Cervical Ganglion CA6 Salivary gland CA7 Atrioventricular Node CA9 Skin CAB39L Prostate CABP5 retina CABYR Testis Intersitial CACNA1B Superior Cervical Ganglion CACNA1D Pancreas CACNA1E Superior Cervical Ganglion CACNA1F pineal day CACNA1G Cerebellum CACNA1H Adrenal Cortex CACNA1I Prefrontal Cortex CACNA1S Skeletal Muscle CACNA2D1 Superior Cervical Ganglion CACNA2D3 CD14 Monocytes CACNB1 Skeletal Muscle CACNG2 Cerebellum Peduncles CACNG4 Skeletal Muscle CADM4 Prostate CADPS2 Cerebellum Peduncles CALCA Dorsal Root Ganglion CALCRL Fetal lung CALML5 Skin CAMK1G Whole Brain CAMK4 Testis Intersitial CAMTA2 pineal night CAND2 Heart CANT1 Prostate CAPN5 Colon CAPN6 Placenta CAPN7 Superior Cervical Ganglion CARD14 CD71 Early Erythroid CASP10 CD4 T cells CASP2 Leukemia lymphoblastic MOLT 8 CASP9 Adrenal Cortex CASQ2 Heart CASR Kidney CASS4 Cingulate Cortex CATSPERB Superior Cervical Ganglion CAV3 Superior Cervical Ganglion CBFA2T3 BDCA4 Dentritic Cells CBL Testis Germ Cell CBLC Bronchial Epithelial Cells CBX2 Trachea CCBP2 Superior Cervical Ganglion CCDC132 Trigeminal Ganglion CCDC19 Testis Intersitial CCDC21 CD71 Early Erythroid CCDC25 CD33 Myeloid CCDC28B Lymphoma burkitts Raji CCDC33 Superior Cervical Ganglion CCDC41 CD40 CCDC46 Testis Intersitial CCDC51 Leukemia promyelocytic HL60 CCDC6 Colon CCDC64 CD8 T cells CCDC68 Fetal lung CCDC76 CD8 T cells CCDC81 Superior Cervical Ganglion CCDC87 Testis CCDC88A BDCA4 Dentritic Cells CCDC88C CD56 NK Cells CCDC99 Leukemia lymphoblastic MOLT 9 CCHCR1 Testis CCIN Testis Intersitial CCKAR Uterus Corpus CCL11 Smooth Muscle CCL13 small intestine CCL18 Thymus CCL2 Smooth Muscle CCL21 Lymph node CCL22 X721 B lymphoblasts CCL24 Uterus Corpus CCL27 Skin CCL3 CD33 Myeloid CCL4 CD56 NK Cells CCL7 Smooth Muscle CCND1 Colorectal adenocarcinoma CCNF CD71 Early Erythroid CCNJ Ciliary Ganglion CCNJL Atrioventricular Node CCNL2 CD4 T cells CCNO Testis CCR10 X721 B lymphoblasts CCR3 Whole Blood CCR5 CD8 T cells CCR6 CD19 Bcells neg. sel. CCRL2 CD71 Early Erythroid CCRN4L Appendix CC5 CD71 Early Erythroid CCT4 Superior Cervical Ganglion CD160 CD56 NK Cells CD180 CD19 Bcells neg. sel. CD1C Thymus CD207 Appendix CD209 Lymph node CD22 Lymphoma burkitts Raji CD226 Superior Cervical Ganglion CD244 CD56 NK Cells CD248 Adipocyte CD320 Heart CD3EAP Dorsal Root Ganglion CD3G Thymus CD4 BDCA4 Dentritic Cells CD40 Lymphoma burkitts Raji CD40LG CD41 CD51 CD105 Endothelial CD79B Lymphoma burkitts Raji CD80 X721 B lymphoblasts CD81 CD71 Early Erythroid CDC14A Testis CDC25C Testis Intersitial CDC27 CD71 Early Erythroid CDC34 CD71 Early Erythroid CDC42EP2 Smooth Muscle CDC6 Colorectal adenocarcinoma CDC73 Colon CDCA4 CD71 Early Erythroid CDCP1 Bronchial Epithelial Cells CDH13 Uterus CDH15 Cerebellum CDH18 Subthalamic Nucleus CDH20 Superior Cervical Ganglion CDH22 Cerebellum Peduncles CDH3 Bronchial Epithelial Cells CDH4 Amygdala CDH5 Placenta CDH6 Trigeminal Ganglion CDH7 Skeletal Muscle CDK5R2 Whole Brain CDK6 CD42 CDK8 Colorectal adenocarcinoma CDKL2 Superior Cervical Ganglion CDKL3 Superior Cervical Ganglion CDKL5 Superior Cervical Ganglion CDKN2D CD71 Early Erythroid CDON Tonsil CDR1 Cerebellum CDS1 small intestine CDSN Skin CDX4 Superior Cervical Ganglion CDYL CD71 Early Erythroid CEACAM21 Bone marrow CEACAM3 Whole Blood CEACAM5 Colon CEACAM7 Colon CEACAM8 Bone marrow CEBPA Liver CEBPE Bone marrow CELSR3 Fetal brain CEMP1 Skeletal Muscle CENPE CD71 Early Erythroid CENPI Appendix CENPQ Trigeminal Ganglion CENPT CD71 Early Erythroid CEP170 Fetal brain CEP55 X721 B lymphoblasts CEP63 Whole Blood CEP76 CD71 Early Erythroid CER1 Superior Cervical Ganglion CES1 Liver CES2 Liver CES3 Colon CETN1 Testis CFHR4 Liver CFHR5 Liver CFI Fetal liver CGB Placenta CGRRF1 Testis Intersitial CHAD Trachea CHAF1A Leukemia lymphoblastic MOLT 10 CHAF1B Leukemia lymphoblastic MOLT 11 CHAT Uterus Corpus CHD3 Fetal brain CHD8 Trigeminal Ganglion CHI3L1 Uterus Corpus CHIA Lung CHIT1 Lymph node CHKA Testis Intersitial CHML Superior Cervical Ganglion CHMP1B Superior Cervical Ganglion CHMP6 Heart CHODL Testis Germ Cell CHPF Colorectal adenocarcinoma CHRM2 Skeletal Muscle CHRM3 Prefrontal Cortex CHRM4 Superior Cervical Ganglion CHRM5 Skeletal Muscle CHRNA2 Heart CHRHA4 Skeletal Muscle CHRNA5 Appendix CHRNA6 Temporal Lobe CHRNA9 Appendix CHRNB3 Superior Cervical Ganglion CHST10 Whole Brain CHST12 CD56 NK Cells CHST3 Testis Germ Cell CHST4 Uterus Corpus CHST7 Ovary CHSY1 Placenta CIB2 BDCA4 Dentritic Cells CIDEA Ciliary Ganglion CIDEB Liver CIDEC Adipocyte CISH Leukemia chronic Myelogenous K568 CKAP2 CD71 Early Erythroid CKM Skeletal Muscle CLCA4 Colon CLCF1 Uterus Corpus CLCN1 Skeletal Muscle CLCN2 Olfactory Bulb CLCN5 Appendix CLCN6 Whole Brain CLCNKA Kidney CLCNKB Kidney CLDN10 Kidney CLDN11 Heart CLDN15 small intestine CLDN4 Colorectal adenocarcinoma CLDN7 Colon CLDN8 Salivary gland CLEC11A CD43 CLEC16A Lymphoma burkitts Raji CLEC4M Lymph node CLEC5A CD33 Myeloid CLGN Testis Intersitial CLIC2 CD71 Early Erythroid CLIC5 Skeletal Muscle CLMN Testis Intersitial CLN3 Placenta CLN5 Thyroid CLN6 pineal day CLPB Testis Intersitial CLTCL1 Testis CLUL1 retina CMA1 Adrenal Cortex CMAH Uterus CMAS CD71 Early Erythroid CMKLR1 BDCA4 Dentritic Cells CNGA1 Uterus Corpus CNIH3 Amygdala CNNM1 Prefrontal Cortex CNNM4 pineal day CNR1 Fetal brain CNR2 Uterus Corpus CNTFR Cardiac Myocytes CNTLN Trigeminal Ganglion CNTN2 Thalamus COBLL1 Placenta COG7 Prostate COL11A1 Adipocyte COL13A1 Cardiac Myocytes COL14A1 Uterus COL17A1 Bronchial Epithelial Cells COL19A1 Trigeminal Ganglion COL7A1 Skin COL8A2 retina COL9A1 pineal night COL9A2 retina COLEC10 Appendix COLEC11 Liver COMP Adipocyte COMT Liver COQ4 Thyroid COQ6 Testis CORIN Superior Cervical Ganglion CORO1B CD14 Monocytes CORO2A Bronchial Epithelial Cells COX6B1 Superior Cervical Ganglion CP Fetal liver CRA3 CD44 CPM Adipocyte CPN2 Liver CPNE6 Amygdala CPNE7 Leukemia chronic Myelogenous K569 CPOX Fetal liver CPT1A X721 B lymphoblasts CPZ Placenta CR1 Whole Blood CREBZF CD8 T cells CRH Placenta CRHR1 Cerebellum Peduncles CRIM1 Placenta CRISP2 Testis Intersitial CRLF1 Adipocyte CRLF2 Skeletal Muscle CRTAC1 Lung CRTAP Adipocyte CRY2 pineal night CRYAA Kidney CRYBA2 Pancreatic Islet CRYBA4 Superior Cervical Ganglion CRYBB1 Superior Cervical Ganglion CRYBB2 retina CRYBB3 Superior Cervical Ganglion CSAD Fetal brain CSAG2 Leukemia chronic Myelogenous K570 CSDC2 Heart CSF2 Colorectal adenocarcinoma CSF2RA BDCA4 Dentritic Cells CSF3 Smooth Muscle CSF3R Whole Blood CSN3 Salivary gland CSNK1G3 CD19 Bcells neg. sel. CSPG4 Trigeminal Ganglion CST2 Salivary gland CST4 Salivary gland CST5 Salivary gland CST7 CD56 NK Cells CSTF2T CD105 Endothelial CTAG2 X721 B lymphoblasts CTB5 Whole Blood CTDSPL Colorectal adenocarcinoma CTF1 Superior Cervical Ganglion CTLA4 Superior Cervical Ganglion CTNNA3 Testis Intersitial CTP52 Ciliary Ganglion CTSD Lung CTSG Bone marrow CTSK Uterus Corpus CTTNBP2NL CD8 T cells CUBN Kidney CUEDC1 BDCA4 Dentritic Cells CUL1 Testis Intersitial CUL7 Smooth Muscle CXCL1 Smooth Muscle CXCL3 Smooth Muscle CXCL5 Smooth Muscle CXCL6 Smooth Muscle CXCR3 BDCA4 Dentritic Cells CXCR5 CD19 Bcells neg. sel. CXorf1 pineal day CXorf40A Adrenal Cortex CXorf56 Superior Cervical Ganglion CXorf57 Hypothalamus CYB561 Prostate CYLC1 Testis Seminiferous Tubule CYLD CD4 T cells CYorf15B CD4 T cells CYP19A1 Placenta CYP1A1 Lung CYP1A2 Liver CYP20A1 BDCA4 Dentritic Cells CYP26A1 Fetal brain CYP27A1 Liver CYP27B1 Bronchial Epithelial Cells CYP2A6 Liver CYP2A7 Liver CYP2B7P1 Superior Cervical Ganglion CYP2C19 Atrioventricular Node CYP2C8 Liver CYP2C9 Liver CYP2D6 Liver CYP2E1 Liver CYP2F1 Superior Cervical Ganglion CYP2W1 Skin CYP3A43 Liver CYP3A5 small intestine CYP3A7 Fetal liver CYP4F11 Liver CYP4F2 Liver CYP4F8 Prostate CYP7B1 Ciliary Ganglion DACT1 Fetal brain DAGLA Amygdala DAO Kidney DAPK2 Atrioventricular Node DAZ1 Testis Leydig Cell DAZL Testis DBI CD71 Early Erythroid DBNDD1 Trigeminal Ganglion DBP Thyroid DCBLD2 Trigeminal Ganglion DCC Testis Seminiferous Tubule DCHS2 Cerebellum DCI Liver DCLRE1A X721 B lymphoblasts DCP1A CD4 T cells DCT retina DCUN1D1 CD71 Early Erythroid DCUN1D2 Heart DCX Fetal brain DDX10 Leukemia promyelocytic HL61 DOX17 Heart DDX23 Thymus DDX25 Testis Leydig Cell DDX28 CD14 Monocytes DDX31 Superior Cervical Ganglion DDX43 Testis Seminiferous Tubule DDX5 Liver DDX51 BDCA4 Dentritic Cells DDX52 Colorectal adenocarcinoma DECR2 Liver DEFA4 Bone marrow DEFA5 small intestine DEFA6 small intestine DEFB126 Testis Germ Cell DEGS1 Skin DENND1A X721 B lymphoblasts DENND2A Atrioventricular Node DENND3 CD33 Myeloid DENND4A pineal night DEPDC5 Lymphoma burkitts Raji DES Skeletal Muscle DGAT1 small intestine DGCR14 Testis Intersitial DGCR6L Trigeminal Ganglion DGCR8 Leukemia chronic Myelogenous K571 DGKA CD4 T cells DGKB Caudate nucleus DGKE Superior Cervical Ganglion DGKG Cerebellum DGKQ Superior Cervical Ganglion DHDDS pineal day DHODH Liver DHRS1 Liver DHRS12 Liver DHRS2 Colorectal adenocarcinoma DHRS9 Trachea DHTKD1 Liver DHX29 CD71 Early Erythroid DHX35 Leukemia lymphoblastic MOLT 12 DHX38 CD56 NK Cells DHX57 Testis Seminiferous Tubule DIAPH2 Testis Germ Cell DIDO1 CD8 T cells DIO2 Thyroid DIO3 Cerebellum Peduncles DKFZP434L187 Atrioventricular Node DKK2 Ciliary Ganglion DKK4 Pancreas DLAT Adipocyte DLEU2 CD71 Early Erythroid DLG3 Fetal brain DLK2 Testis Leydig Cell DLL3 Fetal brain DLX2 Fetal brain DLX4 Placenta DLX5 Placenta DMC1 Superior Cervical Ganglion DMD Olfactory Bulb DMPK Heart DMWD Atrioventricular Node DNA2 X721 B lymphoblasts DNAH17 Testis DNAH2 Atrioventricular Node DNAH9 Cardiac Myocytes DNAI1 Testis DNAI2 Testis DNAJC1 CD56 NK Cells DNAJC9 CD71 Early Erythroid DNAL4 Testis DNALI1 Testis Intersitial DNASE1L1 CD14 Monocytes DNASE1L2 Tonsil DNASE1L3 BDCA4 Dentritic Cells DNASE2B Salivary gland DND1 Testis DNM2 BDCA4 Dentritic Cells DNMT3A Superior Cervical Ganglion DNMT3B Leukemia chronic Myelogenous K572 DNMT3L Liver DOC2B Adrenal gland DOCK5 Superior Cervical Ganglion DOCK6 Lung DOK2 CD14 Monocytes DOK3 Superior Cervical Ganglion DOK4 Fetal brain DOK5 Fetal brain DOLK Testis DOPEY2 Skeletal Muscle DOT1L Superior Cervical Ganglion DPAGT1 X721 B lymphoblasts DPEP3 Testis DPF3 Cerebellum DPH2 Skeletal Muscle DPM2 CD71 Early Erythroid DPP4 Smooth Muscle DPPA4 CD45 DPT Adipocyte DPY19L2P2 Leukemia lymphoblastic MOLT 13 DRD2 Caudate nucleus DSC1 Skin DSG1 Skin DTL CD105 Endothelial DTX2 Skeletal Muscle DTYMK CD105 Endothelial DUSP10 X721 B lymphoblasts DUSP26 Skeletal Muscle DUSP4 Placenta DUSP7 Bronchial Epithelial Cells DVL3 Placenta DYNC2H1 Pituitary DYRK2 CD8 T cells DYRK4 Testis Intersitial DYSF Whole Blood E2F1 CD71 Early Erythroid E2F2 CD71 Early Erythroid E2F4 CD71 Early Erythroid E2F5 Lymphoma burkitts Daudi E2F8 CD71 Early Erythroid E4F1 CD4 T cells EAF2 CD19 Bcells neg. sel. EBI3 Placenta ECHDC1 Adipocyte ECHS1 Liver ECM1 Tongue ECSIT Heart EDA Trigeminal Ganglion EDA2R Superior Cervical Ganglion EDL3 Testis EDIL3 Occipital Lobe EDN2 Superior Cervical Ganglion EDN3 retina EDNRA Uterus EFCAB1 Superior Cervical Ganglion EFHC1 Testis Intersitial EFHC2 Appendix EFNA4 Prostate EFNB1 Colorectal adenocarcinoma EFNB3 Fetal brain EGF Kidney EGFR Placenta EGLN1 Whole Blood EIF1AY CD71 Early Erythroid EIF2AK1 CD71 Early Erythroid EIF2B4 Testis EIF2C2 CD71 Early Erythroid EIF2C3 Pituitary EIF3K Superior Cervical Ganglion EIF4G2 Liver EIF5A2 Ciliary Ganglion ELF3 Colon ELL2 Pancreatic Islet ELMO3 CD71 Early Erythroid ELOVL6 Adipocyte ELSPBP1 Testis Leydig Cell ELTD1 Smooth Muscle EMID1 Fetal brain EMILIN2 Superior Cervical Ganglion EML1 Fetal brain EMR3 Whole Blood EMX2 Uterus EN1 Adipocyte ENDOG Liver ENO3 Skeletal Muscle ENOX1 Fetal brain ENPP1 Thyroid ENTPD1 X721 B lymphoblasts ENTPD2 Superior Cervical Ganglion ENTPD3 Caudate nucleus ENTPD4 Smooth Muscle ENTPD7 Bone marrow EPB41 CD71 Early Erythroid EPB41L4A Trigeminal Ganglion EPHA1 Liver EPHA3 Fetal brain EPHA5 Fetal brain EPN2 CD71 Early Erythroid EPN3 Thalamus EPS15L1 Appendix EPS8L1 Placenta EPS8L3 Pancreas EPX Bone marrow EPYC Placenta ERCC1 Heart ERCC4 Superior Cervical Ganglion ERCC6 Ovary ERCC8 Uterus Corpus EREG CD46 ERF Ciliary Ganglion ERG CD47 ERICH1 Superior Cervical Ganglion ERLIN2 Thyroid ERMAP CD71 Early Erythroid ERMP1 CD56 NK Cells ERN1 Liver ERO1LB Pancreatic Islet ESM1 CD105 Endothelial ESR1 Uterus ETFB Liver ETNK1 Colon ETNK2 Liver ETV3 Superior Cervical Ganglion ETV4 Colorectal adenocarcinoma EVPL Tongue EXOSC1 Trigeminal Ganglion EXOSC2 X721 B lymphoblasts EXOSC4 Testis EXOSC5 X721 B lymphoblasts EXPH5 Placenta EXT2 Smooth Muscle EXTL3 Subthalamic Nucleus EYA3 Cardiac Myocytes EYA4 Skin F10 Liver F11 Pancreas F12 Liver F13B Fetal liver F2R Cardiac Myocytes F2RL1 Colon FAAH pineal night FABP6 small intestine FABP7 Fetal brain FADS1 Adipocyte FAH Liver FAIM Colorectal adenocarcinoma FAM105A BDCA4 Dentritic Cells FAM106A Atrioventricular Node FAM108B1 Whole Brain FAM110B Trigeminal Ganglion FAM118A CD33 Myeloid FAM119B Uterus Corpus FAM120C Ovary FAM125B Spinal Cord FAM127B Thyroid FAM135A Appendix FAM149A pineal day FAM48A Testis Intersitial FAM50B Whole Brain FAM55D Colon FAM5C Amygdala FAMS3A Whole Blood FAM86A Pituitary FAM86B1 Skeletal Muscle FAM86C Leukemia promyelocytic HL62 FANCE Lymphoma burkitts Daudi FANCG Leukemia lymphoblastic MOLT 14 FARP2 Testis FARS2 Heart FAS Whole Blood FASLG CD56 NK Cells FASTK Heart FASTKD2 X721 B lymphoblasts FAT4 Fetal brain FBLN2 Adipocyte FBN2 Placenta FBP1 Liver FBP2 Skeletal Muscle FBXL12 Thymus FBXL15 Whole Brain FBXL4 CD71 Early Erythroid FBXL6 Pancreas FBXL8 X721 B lymphoblasts FBXO17 Leukemia chronic Myelogenous K573 FBXO38 CD8 T cells FBXO4 Trigeminal Ganglion FBXO46 X721 B lymphoblasts FCGR2A Whole Blood FCGR2B Placenta FCHO1 Lymphoma burkitts Raji FCN2 Liver FCRL2 CD19 Bcells neg. sel. FECH CD71 Early Erythroid FEM1B Testis Intersitial FFM1C Cerebellum FER1L4 Trigeminal Ganglion FETUB Liver FEZF2 Amygdala FFAR2 Whole Blood FFAR3 Temporal Lobe FGD1 Fetal brain FGD2 CD33 Myeloid FGF12 Occipital Lobe FGF14 Cerebellum FGF17 Cingulate Cortex FGF2 Smooth Muscle FGF22 Ovary FGF23 Superior Cervical Ganglion FGF3 Colorectal adenocarcinoma FGF4 Olfactory Bulb FGF5 Superior Cervical Ganglion FGF8 Superior Cervical Ganglion FGF9 Cerebellum Peduncles FGFR1OP Testis Intersitial FGFR4 Liver FGL1 Fetal liver FGL2 CD14 Monocytes FHIT CD4 T cells FHL3 Skeletal Muscle FHL5 Testis Intersitial FILIP1L Uterus FKBP10 Smooth Muscle FKBP14 Smooth Muscle FKBP6 Testis FKBPL CD105 Endothelial FKRP Superior Cervical Ganglion FLG Skin FLJ20712 Temporal Lobe FLNC Skeletal Muscle FLOT2 Whole Blood FLT1 Superior Cervical Ganglion FLT4 Placenta FMO2 Lung FMO3 Liver FMO6P Appendix FN3K Superior Cervical Ganglion FNBP1L Fetal brain FNDC8 Testis Intersitial FOLH1 Prostate FOSL1 Colorectal adenocarcinoma FOXA1 Prostate FOXA2 Pancreatic Islet FOXB1 Superior Cervical Ganglion FOXC1 Salivary gland FOXC2 Superior Cervical Ganglion FOXD3 Superior Cervical Ganglion FOXD4 Globus Pallidus FOXE1 Thyroid FOXE3 Superior Cervical Ganglion FOXK2 Adrenal Cortex FOXL1 Liver FOXN1 Superior Cervical Ganglion FOXN2 Appendix FOXP3 Adrenal Cortex FPGS Ovary FPGT pineal day FPR2 Whole Blood FPR3 Superior Cervical Ganglion FRAT1 Whole Blood FRAT2 Whole Blood FRK Superior Cervical Ganglion FRMD8 Superior Cervical Ganglion FRS2 Pituitary FRS3 Testis FRZB retina FSHB Pituitary FSHR Superior Cervical Ganglion FST Bronchial Epithelial Cells FSTL3 Placenta FSTL4 Appendix FTCD Liver FTSJ1 Bronchial Epithelial Cells FXC1 Superior Cervical Ganglion FXN CD105 Endothelial FXYD2 Kidney FYCO1 Tongue FZD4 Adipocyte FZD5 Colon FZD7 Cerebellum FZD8 Superior Cervical Ganglion FZD9 Appendix FZR1 CD71 Early Erythroid G6PC Liver G6PC2 Superior Cervical Ganglion GAB1 Superior Cervical Ganglion GABRA4 Caudate nucleus GABRA5 Amygdala GABRB2 Skin GABRE Placenta GABRG3 Subthalamic Nucleus GABRP Tonsil GABRQ Skeletal Muscle GAD2 Caudate nucleus GADD45G Placenta GADD45GIP1 Heart GAL3ST1 Spinal Cord GALK1 Liver GALK2 Leukemia chronic Myelogenous K574 GALNS CD33 Myeloid GALNT12 Colon GALNT14 Kidney GALNT4 CD71 Early Erythroid GALNT6 CD71 Early Erythroid GALNT8 Trigeminal Ganglion GALR2 Superior Cervical Ganglion GALT Liver GAMT Liver GAPDHS Testis Intersitial GAPVD1 CD71 Early Erythroid GARNL3 Appendix GAST Cerebellum GATA4 Heart GATAD1 Leukemia chronic Myelogenous K575 GATC Superior Cervical Ganglion GBA Placenta GBX1 Bone marrow GCAT Liver GCDH Liver GCGR Liver GCHFR Liver GCKR Liver GCLC CD71 Early Erythroid GCLM CD71 Early Erythroid GCM1 Placenta GCM2 Skeletal Muscle GCNT1 CD19 Bcells neg. sel. GCNT2 CD71 Early Erythroid GDAP1L1 Fetal brain GDF11 retina GDF15 Placenta GDF2 Subthalamic Nucleus GDF5 Fetal liver GDF9 Testis Leydig Cell GDPD3 Colon GEM Uterus Corpus GEMIN4 Testis Intersitial GEMIN8 Skeletal Muscle GFOD2 Superior Cervical Ganglion GFRA3 Liver GFRA4 Pons GGTLC1 Lung GH2 Placenta GHRHR Pituitary GHSR Superior Cervical Ganglion GIF Superior Cervical Ganglion GIMAP4 Whole Blood GINS4 X721 B lymphoblasts GIP small intestine GIPC2 small intestine GJA3 Superior Cervical Ganglion GJA4 Lung GJA5 Superior Cervical Ganglion GJA8 Skeletal Muscle GJB1 Liver GJB3 Bronchial Epithelial Cells GJB5 Bronchial Epithelial Cells GJC1 Superior Cervical Ganglion GJC2 Spinal Cord GK Whole Blood GK2 Testis Intersitial GK3P Testis Germ Cell GKN1 small intestine GLE1 Testis Intersitial GLI1 Atrioventricular Node GLMN Skeletal Muscle GLP2R Superior Cervical Ganglion GLRA1 Superior Cervical Ganglion GLRA2 Uterus Corpus GLS2 Liver GLT8D2 Smooth Muscle GLTP Tonsil GLTPD1 Heart GMD5 Colon GMEB1 CD56 NK Cells GML Trigeminal Ganglion GNA13 BDCA4 Dentritic Cells GNA14 Superior Cervical Ganglion GNAT1 retina GNA2 Fetal brain GNB1L Leukemia chronic Myelogenous K576 GNG4 Superior Cervical Ganglion GNLY CD56 NK Cells GNRHR Pituitary GOLT1B Smooth Muscle GON4L Leukemia chronic Myelogenous K577 GP5 Trigeminal Ganglion GP6 Superior Cervical Ganglion GP9 Whole Blood GPATCH1 CD8 T cells GPATCH2 Testis Seminiferous Tubule GPATCH3 CD14 Monocytes GPATCH4 Atrioventricular Node GPATCH8 CD56 NK Cells GPC4 Pituitary GPC5 pineal day GPD1 Adipocyte GPI CD71 Early Erythroid GPKOW CD71 Early Erythroid GPR124 retina GPR137 Testis GPR143 retina GPR153 Fetal brain GPR157 Globus Pallidus GPR161 Uterus GPR17 Whole Brain GPRU172B Placenta GPR176 Smooth Muscle GPR18 CD19 Bcells neg. sel. GPR182 Superior Cervical Ganglion GPR20 Trigeminal Ganglion GPR21 Globus Pallidus GPR31 Superior Cervical Ganglion GPR32 Superior Cervical Ganglion GPR35 Pancreas GPR37L1 Amygdala GPR39 Superior Cervical Ganglion GPR4 Lung GPR44 Thymus GPR50 Superior Cervical Ganglion GPR52 Superior Cervical Ganglion GPR6 Caudate nucleus GPR64 Testis Leydig Cell GPR65 CD56 NK Cells GPR68 Skeletal Muscle GPR87 Bronchial Epithelial Cells GPR98 Medulla Oblongata GPRIN2 Superior Cervical Ganglion GPT Liver GPX5 Testis Leydig Cell GRAMD1C Appendix GRB7 Liver GREM1 Smooth Muscle GRID2 Superior Cervical Ganglion GRIK3 Superior Cervical Ganglion GRIK4 Olfactory Bulb GRIN2A Subthalamic Nucleus GRIN2B Skeletal Muscle GRIN2C Thyroid GRIN2D Superior Cervical Ganglion GRIP1 Superior Cervical Ganglion GRIP2 CD48 GRK1 Superior Cervical Ganglion GRK4 Testis GRM1 Cerebellum GRM2 Heart GRM4 Cerebellum Peduncles GRRP1 Globus Pallidus GRTP1 Superior Cervical Ganglion GSR X721 B lymphoblasts GSTCD Atrioventricular Node GSTM1 Liver GSTM2 Liver GSTM4 small intestine GSTT2 Whole Brain GSTTP1 Testis Intersitial GSTZ1 Liver GTF2IRD1 Colorectal adenocarcinoma GTF3C5 Heart GTPBP1 CD71 Early Erythroid GUCY1A2 Superior Cervical Ganglion GUCY1B2 Superior Cervical Ganglion GUCY2C Colon GUCY2D BDCA4 Dentritic Cells GUF1 Superior Cervical Ganglion GULP1 Placenta GYG2 Adipocyte GYPE CD71 Early Erythroid GYS1 Heart GZMK CD8 T cells H2AFB1 Testis HAAO Liver HAL Fetal liver HAMP Liver HAO1 Liver HAO2 Kidney HAPLN1 Cardiac Myocytes HAPLN2 Spinal Cord HAS2 Skeletal Muscle HBE1 Leukemia chronic Myelogenous K578 HBQ1 CD71 Early Erythroid HBS1L CD71 Early Erythroid HBXIP Kidney HCCS CD71 Early Erythroid HCFC2 Testis Intersitial HCG4 Superior Cervical Ganglion HCG9 Liver HCN4 Testis Leydig Cell HCRT Hypothalamus HCRTR1 Bone marrow HCRTR2 Atrioventricular Node HDAC11 Testis HDGF CD71 Early Erythroid HEATR6 Atrioventricular Node HECTD3 CD71 Early Erythroid HECW1 Atrioventricular Node HEPH Leukemia chronic Myelogenous K579 HEXIM1 CD71 Early Erythroid HEY2 retina HGC6.3 Skeletal Muscle HGF Smooth Muscle HGFAC Liver HHAT BDCA4 Dentritic Cells HHIPL2 Testis Intersitial HHLA1 Adrenal gland HHLA3 Liver HIC1 Superior Cervical Ganglion HIC2 Leukemia chronic Myelogenous K580 HIF3A Superior Cervical Ganglion HIGD1B Lung HIP1R CD19 Bcells neg. sel. HIPK3 CD33 Myeloid HIST1H1E Leukemia chronic Myelogenous K581 HIST1H1T Dorsal Root Ganglion HIST1H2AB CD19 Bcells neg. sel. HIST1H2BC Leukemia chronic Myelogenous K582 HIST1H2BG CD8 T cells HIST1H2BJ Ciliary Ganglion HIST1H2BM Superior Cervical Ganglion HIST1H2BN small intestine HIST1H3F Uterus Corpus HIST1H3I Cardiac Myocytes HIST1H3J Atrioventricular Node HIST1H4A CD71 Early Erythroid HIST1H4E Superior Cervical Ganglion HIST1H4G Skeletal Muscle HIST3H2A Leukemia chronic Myelogenous K583 HIVEP2 Fetal brain HKDC1 pineal night HLA-DOB CD19 Bcells neg. sel. HLCS Thyroid HMBS CD71 Early Erythroid HMGA2 Bronchial Epithelial Cells HMGB3 Placenta HMGCL Liver HMGCS2 Liver HMHB1 Skeletal Muscle HNF4G Ovary HNRNPA2B1 Liver HOOK1 Testis Intersitial HOOK2 Thyroid HOXA1 Leukemia chronic Myelogenous K584 HOXA10 Uterus HOXA3 Superior Cervical Ganglion HOXA6 Kidney HOXA7 Adrenal Cortex HOXA9 Colorectal adenocarcinoma HOXB1 Cingulate Cortex HOXB13 Prostate HOXB5 Colorectal adenocarcinoma HOXB6 Colorectal adenocarcinoma HOXB7 Colorectal adenocarcinoma HOXB8 Superior Cervical Ganglion HOXC11 Superior Cervical Ganglion HOXC5 Liver HOXC8 Skeletal Muscle HOXD1 Trigeminal Ganglion HOXD10 Uterus HOXD11 Appendix HOXD12 Skeletal Muscle HOXD3 Uterus HOXD4 Uterus HOXD9 Uterus HP Liver HPGD Placenta HPN Liver HPR Liver HPS1 CD71 Early Erythroid HPS4 CD105 Endothelial HR pineal day HRC Heart HRG Liver HRK CD19 Bcells neg. sel. HS1BP3 CD14 Monocytes HS3ST1 Ovary HS3ST3B1 Heart HS6ST1 Superior Cervical Ganglion HSD11B1 Liver HSD17B1 Placenta HSD17B2 Placenta HSD17B6 Liver HSD17B8 Liver HSD3B1 Placenta HSF1 Heart HSFX1 Cardiac Myocytes HSP90AA1 Heart HSPA1L Testis Intersitial HSPA4L Testis Intersitial HSPA6 Whole Blood HSPB2 Heart HSPB3 Heart HSPC159 Superior Cervical Ganglion HTN1 Salivary gland HTR1A Liver HTR1B Heart HTR1D Skeletal Muscle HTR1E pineal night HTR1F Appendix HTR2A Prefrontal Cortex HTR2C Caudate nucleus HTR3A Dorsal Root Ganglion HTR3B Skin HTR5A Skeletal Muscle HTR7 Cardiac Myocytes HTRA2 CD71 Early Erythroid HUS1 Superior Cervical Ganglion HYAL2 Lung HYAL4 Superior Cervical Ganglion ICAM4 CD71 Early Erythroid ICAM5 Amygdala ICOSLG Skeletal Muscle IDE Testis Germ Cell IDH3G Heart IER3IP1 Smooth Muscle IFI44 CD33 Myeloid IFIT1 Whole Blood IFIT2 Whole Blood IFIT5 Whole Blood IFNA21 Testis Seminiferous Tubule IFNA4 Dorsal Root Ganglion IFNA5 Superior Cervical Ganglion IFNA6 Superior Cervical Ganglion IFNAR1 Superior Cervical Ganglion IFNG CD56 NK Cells IFNW1 Ovary IFT140 Thyroid IFT52 CD71 Early Erythroid IFT81 Testis Leydig Cell IGF1R Prostate IGF2AS Subthalamic Nucleus IGFALS Liver IGLL1 CD49 IGLV6-57 Lymph node IHH Heart IKZF3 CD8 T cells IKZF5 CD8 T cells IL10 Atrioventricular Node IL11 Smooth Muscle IL11RA CD4 T cells IL12A Uterus Corpus IL12RB2 CD56 NK Cells IL13 Testis Intersitial IL13RA2 Testis Intersitial IL15 pineal night IL17B Olfactory Bulb IL17RA CD33 Myeloid IL17RB Kidney IL18RAP CD56 NK Cells IL19 Trachea IL1B Smooth Muscle IL1F6 Superior Cervical Ganglion IL1F7 Skeletal Muscle IL1F9 Superior Cervical Ganglion IL1RAPL1 Prefrontal Cortex IL1RAP12 Superior Cervical Ganglion IL1RL1 Placenta IL2 Heart IL20RA Ciliary Ganglion IL21 Superior Cervical Ganglion IL22 Superior Cervical Ganglion IL24 Smooth Muscle IL25 Pons IL2RA Superior Cervical Ganglion IL2RB CD56 NK Cells IL3RA BDCA4 Dentritic Cells IL4 Atrioventricular Node IL4R CD19 Bcells neg. sel. IL5 Atrioventricular Node IL5RA Ciliary Ganglion IL9 Leukemia promyelocytic HL63 IL9R Testis Intersitial ILVBL Heart IMPG1 retina INCENP Leukemia lymphoblastic MOLT 15 INE1 Atrioventricular Node ING1 CD19 Bcells neg. sel. INHA Testis Germ Cell INHBA Placenta INHBE Liver INPP5B X721 B lymphoblasts INSIG2 X721 B lymphoblasts INSL4 Placenta INSL6 Superior Cervical Ganglion INSRR Superior Cervical Ganglion INTS12 BDCA4 Dentritic Cells INTS5 Liver IPO8 CD4 T cells IQCB1 Lymphoma burkitts Daudi IRF2 Whole Blood IRF6 Bronchial Epithelial Cells IRS4 Skeletal Muscle IRX4 Skin IRX5 Lung ISCA1 CD71 Early Erythroid ISL1 Pancreatic Islet ISOC2 Liver ISYNA1 Testis Germ Cell ITCH Testis Intersitial ITFG2 CD4 T cells ITGA2 Bronchial Epithelial Cells ITGA3 Bronchial Epithelial Cells ITGA9 Testis Seminiferous Tubule ITGB1BP3 Heart ITGB5 Colorectal adenocarcinoma ITGB6 Bronchial Epithelial Cells ITGB8 Appendix ITGBL1 Adipocyte ITIH4 Liver ITIH5 Placenta ITM2B X721 B lymphoblasts ITPKA Whole Brain ITSN1 CD71 Early Erythroid IVL Tongue JAKMIP2 Prefrontal Cortex JMJD5 Liver JPH2 Superior Cervical Ganglion KAL1 Spinal Cord KAZALD1 Skeletal Muscle KCNA1 Superior Cervical Ganglion KCNA10 Skeletal Muscle KCNA2 Skeletal Muscle KCNA3 Dorsal Root Ganglion KCNA4 Superior Cervical Ganglion KCNAB1 Caudate nucleus KCNAB3 Subthalamic Nucleus KCNB2 Trigeminal Ganglion KCNC3 Lymphoma burkitts Daudi KCND1 Thyroid KCND2 Cerebellum Peduncles KCNE1 Pancreas KCNE1L Superior Cervical Ganglion KCNE4 Uterus Corpus KCNG1 CD19 Bcells neg. sel. KCNG2 Superior Cervical Ganglion KCNH1 Appendix KCNH2 CD105 Endothelial KCNH4 Superior Cervical Ganglion KCNJ1 Kidney KCNJ10 Occipital Lobe KCNJ13 Superior Cervical Ganglion KCNJ14 Appendix KCNJ2 Whole Blood KCNJ3 Superior Cervical Ganglion KCNJ6 Cingulate Cortex KCNJ9 Cerebellum KCNK10 BDCA4 Dentritic Cells KCNK12 Olfactory Bulb KCNK2 Atrioventricular Node KCNK7 Superior Cervical Ganglion KCNMA1 Uterus KCNMB3 Testis Intersitial KCNN2 Adrenal gland KCNN4 CD71 Early Erythroid KCNS3 Lung KCNV2 retina KCTD14 Adrenal gland KCTD15 Kidney KCTD17 pineal day KCTD20 CD71 Early Erythroid KCTD5 BDCA4 Dentritic Cells KCTD7 pineal night KDELC1 Cardiac Myocytes KDELR3 Smooth Muscle KDSR Olfactory Bulb KIAA0040 CD19 Bcells neg. sel. KIAA0087 Trigeminal Ganglion KIAA0090 Placenta KIAA0100 BDCA4 Dentritic Cells KIAA0141 Superior Cervical Ganglion KIAA0196 CD14 Monocytes KIAA0319 Fetal brain KIAA0556 pineal day KIAA0586 Testis Intersitial KIAA1024 Adrenal Cortex KIAA1199 Smooth Muscle KIAA1310 Uterus Corpus KIAA1324 Prostate KIAA1539 CD71 Early Erythroid KIAA1609 Bronchial Epithelial Cells KIAA1751 Superior Cervical Ganglion KIF17 Cingulate Cortex KIF18A X721 B lymphoblasts KIF18B Leukemia lymphoblastic MOLT 16 KIF21B Fetal brain KIF22 CD71 Early Erythroid KIF25 Superior Cervical Ganglion KIF26B Ciliary Ganglion KIF5A Whole Brain KIFC1 CD71 Early Erythroid KIR2DL2 CD56 NK Cells KIR2DL3 CD56 NK Cells KIR2DL4 CD56 NK Cells KIR2DS4 CD56 NK Cells KIR3DL1 CD56 NK Cells KIR3DL2 CD56 NK Cells KIRREL Superior Cervical Ganglion KISS1 Placenta KL Kidney KLF12 CD8 T cells KLF15 Liver KLF3 CD71 Early Erythroid KLF8 Spinal Cord KLHDC4 CD56 NK Cells KLHL11 Temporal Lobe KLHL12 Testis Intersitial KLHL18 CD105 Endothelial KLHL21 Heart KLHL25 Atrioventricular Node KLHL26 Whole Brain KLHL29 Uterus Corpus KLHL3 Cerebellum KLHL4 Fetal brain KLK10 Tongue KLK12 Tongue KLK13 Tongue KLK14 Atrioventricular Node KLK15 Pancreas KLK2 Prostate KLK3 Prostate KLK5 Testis Intersitial KLK7 Pancreas KLK8 Tongue KLRC3 CD56 NK Cells KLRF1 CD56 NK Cells KLRK1 CD8 T cells KNTC1 Leukemia lymphoblastic MOLT 17 KPNA4 X721 B lymphoblasts KPTN Cerebellum KRT1 Skin KRT10 Skin KRT12 Liver KRT17 Tongue KRT2 Skin KRT23 Colorectal adenocarcinoma KRT3 Superior Cervical Ganglion KRT33A Superior Cervical Ganglion KRT34 Skin KRT36 Superior Cervical Ganglion KRT38 Atrioventricular Node KRT6B Tongue KRT84 Superior Cervical Ganglion KRT86 Placenta KRT9 Superior Cervical Ganglion KRTAP1-1 Superior Cervical Ganglion KRTAP1-3 Ciliary Ganglion KRTAP4-7 Superior Cervical Ganglion KRTAP5-9 Superior Cervical Ganglion L1TD1 Dorsal Root Ganglion L2HGDH Superior Cervical Ganglion LACTB2 small intestine LAD1 Bronchial Epithelial Cells LAIR1 BDCA4 Dentritic Cells LAIR2 CD56 NK Cells LALBA Ovary LAMA2 Adipocyte LAMA3 Bronchial Epithelial Cells LAMA4 Smooth Muscle LAMA5 Colorectal adenocarcinoma LAMB3 Bronchial Epithelial Cells LAMC2 Bronchial Epithelial Cells LANCL2 Testis LAT CD4 T cells LAX1 CD4 T cells LCAT Liver LCMT2 CD105 Endothelial LCT Trigeminal Ganglion LDB1 CD105 Endothelial LDB3 Skeletal Muscle LDHAL6B Testis LDHB Liver LDLR Adrenal Cortex LECT1 CD105 Endothelial LEF1 Thymus LEFTY1 Colon LEFTY2 Uterus Corpus LENEP Salivary gland LEP Placenta LETM1 Thymus LFNG Liver LGALS13 Placenta LGALS14 Placenta LGR4 Colon LHB Pituitary LHCGR Superior Cervical Ganglion LHX2 Fetal brain LHX5 Superior Cervical Ganglion LHX6 Fetal brain LIG3 Leukemia lymphoblastic MOLT 18 LILRB4 BDCA4 Dentritic Cells LILRB5 Skeletal Muscle LIM2 CD56 NK Cells LIMS2 Uterus LIPF small intestine LIPG Thyroid LIPT1 CD8 T cells LMCD1 Skeletal Muscle LMF1 Liver LMO1 retina LMTK2 Superior Cervical Ganglion LMX1B Superior Cervical Ganglion LOC1720 Superior Cervical Ganglion LOC388796 Lymphoma burkitts Raji LOC390561 Uterus Corpus LOC390940 Superior Cervical Ganglion LOC399904 Temporal Lobe LOC441204 Appendix LOC442421 Superior Cervical Ganglion LOC51145 Appendix LOC93432 Ovary LOH3CR2A Appendix LOR Skin LPAL2 Uterus Corpus LPAR3 Testis Germ Cell LPIN2 CD71 Early Erythroid LRAT Pons LRCH3 CD8 T cells LRDD Pancreas LRFN3 Superior Cervical Ganglion LRFN4 Fetal brain LRIT1 Superior Cervical Ganglion LRP1B Amygdala LRP2 Thyroid LRP5L Superior Cervical Ganglion LRRC16A Testis Germ Cell LRRC17 Smooth Muscle LRRC2 Thyroid LRRC20 Skeletal Muscle LRRC3 Skeletal Muscle LRRC31 Colon LRRC32 Lung LRRC36 Testis Intersitial LRRC37A4 Cerebellum LRRK1 Lymphoma burkitts Daudi LST1 Whole Blood LST-3TM12 Fetal liver LTB4R CD33 Myeloid LTB4R2 Temporal Lobe LTBP4 Thyroid LTC4S Lung LTK BDCA4 Dentritic Cells LUC7L Whole Blood LY6D Tongue LY6E Lung LY6G5C CD71 Early Erythroid LY6G6D Pancreas LY6G6E Ovary LY6H Amygdala LY96 Whole Blood LYL1 CD71 Early Erythroid LYPD1 Smooth Muscle LYST Whole Blood LYVE1 Fetal lung LYZL6 Testis Intersitial LZTFL1 Leukemia lymphoblastic MOLT 19 LZTS1 Skeletal Muscle MACROD1 Heart MAF small intestine MAFF Placenta MAFK Superior Cervical Ganglion MAGEA1 X721 B lymphoblasts MAGEA2 Leukemia chronic Myelogenous K585 MAGEA5 X721 B lymphoblasts MAGEA8 Placenta MAGEB1 Testis Germ Cell MAGEC1 Leukemia chronic Myelogenous K586 MAGEC2 Skeletal Muscle MAGED4 Fetal brain MAGEL2 Hypothalamus MAGI1 Globus Pallidus MAGIX Superior Cervical Ganglion MAGOHB CD105 Endothelial MALL small intestine MAML3 Ovary MAMLD1 Testis Germ Cell MAN1A2 Placenta MAN1C1 Placenta MAN2C1 CD8 T cells MAP2K3 CD71 Early Erythroid MAP2K5 Globus Pallidus MAP2K7 Atrioventricular Node MAP3K12 Cerebellum MAP3K14 CD19 Bcells neg. sel. MAP3K6 Lung MAP4K2 X721 B lymphoblasts MAPK4 Skeletal Muscle MAPK7 CD56 NK Cells MAPKAP1 X721 B lymphoblasts MAPKAPK3 Heart MARK2 Globus Pallidus MARK3 CD71 Early Erythroid MAS1 Appendix MASP1 Heart MASP2 Liver MAST1 Fetal brain MATK CD56 NK Cells MATN1 Trachea MATN4 Lymphoma burkitts Raji MBNL3 CD71 Early Erythroid MBTPS1 pineal night MBTPS2 Dorsal Root Ganglion MC2R Adrenal Cortex MC3R Superior Cervical Ganglion MC4R Superior Cervical Ganglion MCCC2 X721 B lymphoblasts MCF2 pineal day MCM10 CD105 Endothelial MCM9 GD19 Bcells neg. sel. MCOLN3 Adrenal Cortex MCPH1 Thymus MCTP1 Caudate nucleus MCTP2 Whole Blood ME1 Adipocyte MECR Heart MED1 Thymus MED15 CD8 T cells MED22 CD19 Bcells neg. sel. MED31 Cerebellum MED7 Testis Intersitial MEGF6 Lung MEGF8 Skeletal Muscle MEOX2 Fetal lung MEP1B small intestine MET Bronchial Epithelial Cells METTL4 CD8 T cells METTL8 CD19 Bcells neg. sel. MEX3D Subthalamic Nucleus MFAP5 Adipocyte MF12 Uterus Corpus MFN1 Lymphoma burkitts Raji MFSD7 Ovary MGA CD8 T cells MGAT4A CD8 T cells MGAT5 Temporal Lobe MGC29506 Thymus MGC4294 Superior Cervical Ganglion MGCSS90 Cardiac Myocytes MGMT Liver MGST3 Lymphoma burkitts Daudi MIA2 Superior Cervical Ganglion MIA3 BDCA4 Dentritic Cells MICALL2 Colorectal adenocarcinoma MIER2 Lung MIPEP Kidney MITF Uterus MKS1 Superior Cervical Ganglion MLANA retina MLF1 Testis Intersitial MLH3 Whole Blood MLL2 Liver MLLT1 Superior Cervical Ganglion MLLT10 Dorsal Root Ganglion MLLT3 CD8 T cells MLN Liver MLNR Superior Cervical Ganglion MMACHC Liver MME Adipocyte MMP10 Uterus Corpus MMP11 Placenta MMP12 Tonsil MMP15 Thyroid MMP24 Cerebellum Peduncles MMP26 Skeletal Muscle MMP28 Lung MMP3 Smooth Muscle MMP8 Bone marrow MMP9 Bone marrow MN1 Fetal brain MNDA Whole Blood MOBKL3 Adrenal Cortex MOCOS Adrenal gland MOCS3 Atrioventricular Node MOGAT2 Liver MON1B Prostate MORC4 Placenta MORF4L2 Heart MORN1 Cingulate Cortex MOS Superior Cervical Ganglion MOSC2 Kidney MOSPD2 CD33 Myeloid MPL Skeletal Muscle MPP3 Cerebellum MPP5 Placenta MPP6 Testis Germ Cell MPPED1 Fetal brain MPPED2 Thyroid MPZL1 Smooth Muscle MPZL2 Colorectal adenocarcinoma MRAS Heart MREG pineal day MRPL17 X721 B lymphoblasts MRPL46 X721 B lymphoblasts MRPS18A Heart MRPS18C Atrioventricular Node MRS2 X721 B lymphoblasts MRTO4 Leukemia promyelocytic HL64 MS4A12 Colon MS4A2 Ciliary Ganglion MS4A4A Placenta MS4A5 Testis Intersitial MSC X721 B lymphoblasts MSH4 Uterus Corpus MSLN Lung MSRA Kidney MST1 Liver MST1R Colorectal adenocarcinoma MSX1 Colorectal adenocarcinoma MT4 Lymphoma burkitts Raji MTERFD1 CD105 Endothelial MTERFD2 CD8 T cells MTF1 CD33 Myeloid MTHFSD Testis MTMR10 CD71 Early Erythroid MTMR12 CD71 Early Erythroid MTMR3 CD71 Early Erythroid MTMR4 Placenta MTMR7 Superior Cervical Ganglion MTMR8 Skeletal Muscle MTNR1A Superior Cervical Ganglion MTNR1B Superior Cervical Ganglion MTTP small intestine MUC1 Lung MUC13 Pancreas MUC16 Trachea MUC2 Colon MUC5B Trachea MUM1 Testis MUSK Skeletal Muscle MUTYH Leukemia lymphoblastic MOLT 20 MVD Adipocyte MXD1 Whole Blood MYBPC1 Skeletal Muscle MYBPC3 Heart MYBPH Superior Cervical Ganglion MYCN Fetal brain MYCT1 Trigeminal Ganglion MYF5 Superior Cervical Ganglion MYF6 Skeletal Muscle MYH1 Skeletal Muscle MYH13 Skeletal Muscle MYH15 Appendix MYH7B Superior Cervical Ganglion MYL7 Heart MYNN Trigeminal Ganglion MYO16 Fetal brain MYO1A small intestine MYO1B Bronchial Epithelial Cells MYO5A Superior Cervical Ganglion MYO5C Salivary gland MYO78 Liver MYOC retina MYST2 Testis MYT1 pineal night N4BP1 Whole Blood N6AMT1 Trigeminal Ganglion NAALAD2 Pituitary NAALADL1 Liver NAB2 Cerebellum NAPG Superior Cervical Ganglion NARF CD71 Early Erythroid NAT1 Colon NAT2 Colon NAT8 Kidney NAT8B Kidney NAV2 Fetal brain NAV3 Fetal brain NBEA Fetal brain NBEAL2 Lymphoma burkitts Raji NCAM2 Superior Cervical Ganglion NCAPG2 CD71 Early Erythroid NCBP1 X721 B lymphoblasts NCLN BDCA4 Dentritic Cells NCOA2 Whole Blood NCR1 CD56 NK Cells NCR2 Lymphoma burkitts Raji NCR3 CD56 NK Cells NDP Amygdala NDUFA4L2 Pancreas NDUFB2 Heart NDUFB7 Heart NECAB2 Caudate nucleus NEIL3 Leukemia lymphoblastic MOLT 21 NEK11 Uterus Corpus NEK3 Pancreas NEK4 Testis Germ Cell NELF Colorectal adenocarcinoma NELL1 Whole Brain NES Olfactory Bulb NETO2 Fetal brain NEU3 Atrioventricular Node NEUROD6 Fetal brain NEUROG3 Superior Cervical Ganglion NFATC1 CD19 Bcells neg. sel. NFATC3 Thymus NFE2 CD71 Early Erythroid NFE2L3 Colorectal adenocarcinoma NFKB2 Lymphoma burkitts Raji NFKBIB Testis NFKBIL2 Atrioventricular Node NFX1 BDCA4 Dentritic Cells NFYA Cardiac Myocytes NGB CD71 Early Erythroid NGF Ciliary Ganglion NGFR Colorectal adenocarcinoma NHLH2 Hypothalamus NINJ1 Whole Blood NIPSNAP3B Superior Cervical Ganglion NKAIN1 Fetal brain NKX2-2 Spinal Cord NKX2-5 Heart NKX2-8 Superior Cervical Ganglion NKX3-2 Colon NKX6-1 Skeletal Muscle NLE1 Lymphoma burkitts Raji NMBR Superior Cervical Ganglion NMD3 Bronchial Epithelial Cells NME5 Testis Intersitial NMU Leukemia chronic Myelogenous K587 NMUR1 CD56 NK Cells NOC2L Lymphoma burkitts Raji NOC3L X721 B lymphoblasts NOC4L Testis NOL10 Superior Cervical Ganglion NOL3 Heart NOS1 Uterus Corpus NOS3 Placenta NOTCH1 Leukemia lymphoblastic MOLT 22 NOX1 Colon NOX3 CD105 Endothelial NOX4 Kidney NPAS2 Smooth Muscle NPAT CD8 T cells NPC1L1 Fetal liver NPFFR1 Subthalamic Nucleus NPHP4 CD50 NPHS2 Kidney NPM3 Bronchial Epithelial Cells NPPA Heart NPPB Heart NPPC Superior Cervical Ganglion NPTXR Skeletal Muscle NPY Prostate NPY1R Fetal brain NPYR Superior Cervical Ganglion NQO2 Kidney NR0B2 Liver NR1D1 pineal day NR1H2 Lung NR1H4 Fetal liver NR1I3 Liver NR2C1 Superior Cervical Ganglion NR2C2 Testis Leydig Cell NR2E1 Amygdala NR2E3 retina NR4A1 Adrenal Cortex NR4A2 Adrenal Cortex NR4A3 Adrenal Cortex NR5A1 Globus Pallidus NR6A1 Testis NRAP Heart NRAS BDCA4 Dentritic Cells NRBF2 Whole Blood NRG2 Superior Cervical Ganglion NRIP2 Olfactory Bulb NRL retina NRP2 Skeletal Muscle NRTN Superior Cervical Ganglion NRXN3 Cerebellum Peduncles NSUN3 CD71 Early Erythroid NSUN6 CD4 T cells NT5DC3 Fetal brain NT5M CD71 Early Erythroid NTAN1 CD71 Early Erythroid NTHL1 Liver NTN1 Superior Cervical Ganglion NTNG1 Uterus Corpus NTSR1 Colorectal adenocarcinoma NUDT1 CD71 Early Erythroid NUDT15 Colorectal adenocarcinoma NUDT18 CD19 Bcells neg. sel. NUDT4 CD71 Early Erythroid NUDT6 Leukemia lymphoblastic MOLT 23 NUDT7 Superior Cervical Ganglion NUFIP1 CD105 Endothelial NUMB Whole Blood NUP155 Testis Intersitial NUPL1 Fetal brain NUPL2 Colorectal adenocarcinoma NXPH3 Cerebellum OAS1 CD14 Monocytes OAS2 Lymphoma burkitts Daudi OAS3 CD33 Myeloid OASL Whole Blood OAZ3 Testis Intersitial OBFC2A Uterus Corpus OBSCN Temporal Lobe OCEL1 CD14 Monocytes OCLM Superior Cervical Ganglion OCLN Skeletal Muscle ODF1 Testis Intersitial ODZ4 Fetal brain OGFRL1 Whole Blood OLAH Placenta OLFM4 small intestine OLFML3 Adipocyte OLR1 Placenta OMD Superior Cervical Ganglion OMP Superior Cervical Ganglion ONECUT1 Liver OPA3 Colorectal adenocarcinoma OPLAH Heart OPN1LW retina OPN1SW Superior Cervical Ganglion OPRD1 Thalamus OPRL1 Lymphoma burkitts Raji OR10C1 Superior Cervical Ganglion OR10H1 Trigeminal Ganglion OR10H3 Pons OR10J1 Superior Cervical Ganglion OR11A1 Superior Cervical Ganglion OR1A1 Superior Cervical Ganglion OR2B2 Superior Cervical Ganglion OR2B6 Superior Cervical Ganglion OR2C1 Superior Cervical Ganglion OR2H1 Skeletal Muscle OR2J3 Superior Cervical Ganglion OR2S2 Uterus Corpus OR2W1 Superior Cervical Ganglion OR3A2 Superior Cervical Ganglion OR52A1 Testis Seminiferous Tubule OR5I1 Lymphoma burkitts Raji OR6A2 Superior Cervical Ganglion OR7A5 Appendix OR7C1 Testis Seminiferous Tubule OR7E19P Superior Cervical Ganglion ORAI2 CD19 Bcells neg. sel. ORM1 Liver OSBP2 CD71 Early Erythroid OSBPL10 CD19 Bcells neg. sel. OSBPL3 Colorectal adenocarcinoma OSBPL7 Tonsil OSGEPL1 CD4 T cells OSM CD71 Early Erythroid OSR2 Uterus OTUD3 Prefrontal Cortex OTUD7B Heart OXCT2 Testis Intersitial OXSM X721 B lymphoblasts OXT Hypothalamus P2RX2 Superior Cervical Ganglion P2RX3 CD71 Early Erythroid P2RX6 Skeletal Muscle P2RY10 CD19 Bcells neg. sel. P2RY2 Bronchial Epithelial Cells P2RY4 Superior Cervical Ganglion PADI3 Pons PAEP Uterus PAFAH2 Thymus PAGE1 X721 B lymphoblasts PAK1IP1 Prostate PAK7 Fetal brain PALB2 X721 B lymphoblasts PALMD Fetal liver PANK4 Lymphoma burkitts Raji PANX1 Bronchial Epithelial Cells PAPOLG Fetal brain PAPPA2 Placenta PAQR3 Testis Germ Cell PARD3 Bronchial Epithelial Cells PARG Superior Cervical Ganglion PARN X721 B lymphoblasts PARP11 Appendix PARP16 Atrioventricular Node PARP3 X721 B lymphoblasts PART1 Prostate PAWR Uterus PAX1 Thymus PAX2 Kidney PAX4 Superior Cervical Ganglion PAX7 Atrioventricular Node PCCA Colon PCDH1 Placenta PCDH11X Fetal brain PCDH17 Testis Intersitial PCDH7 Prefrontal Cortex PCDHB1 Superior Cervical Ganglion PCDHB11 Uterus Corpus PCDHB13 Pancreatic Islet PCDHB3 Testis PCDHB6 Superior Cervical Ganglion PCK2 Liver PCNP Liver PCNT Skeletal Muscle PCNX CD8 T cells PCNXL2 Prefrontal Cortex PCOLCE Liver PCOLCE2 Adipocyte PCSK1 Pancreatic Islet PCYOX1 Adipocyte PCYT1A Testis PDC retina PDCD1 Pons PDCD1LG2 Superior Cervical Ganglion PDE10A Caudate nucleus PDE1B Caudate nucleus PDE1C pineal night PDE3B CD8 T cells PDE6A retina PDE6G retina PDE7B Trigeminal Ganglion PDE9A Prostate PDGFRL Fetal Thyroid PDHA2 Testis Intersitial PDIA2 Pancreas PDK3 X721 B lymphoblasts PDLIM3 Skeletal Muscle PDLIM4 Colorectal adenocarcinoma PDPN Placenta PDPR Superior Cervical Ganglion PDSS1 Leukemia lymphoblastic MOLT 24 PDX1 Heart PDXP CD14 Monocytes PDZD3 Superior Cervical Ganglion PDZK1IP1 Kidney PDZRN4 Atrioventricular Node PECR Liver PEPD Kidney PER3 retina PET112L Heart PEX11A Prostate PEX13 Testis Intersitial PEX19 Adipocyte PEX3 X721 B lymphoblasts PEX5L Superior Cervical Ganglion PF4 Whole Blood PF4V1 Whole Blood PFKFB1 Liver PFKFB2 Pancreatic Islet PFKFB3 Skeletal Muscle PGA3 small intestine PGAM1 CD71 Early Erythroid PGAP1 Adrenal Cortex PGGT1B Ciliary Ganglion PGK2 Testis Intersitial PGLYRP4 Superior Cervical Ganglion PGM3 Smooth Muscle PGPEP1 Kidney PGR Uterus PHACTR4 X721 B lymphoblasts PHC1 Testis Germ Cell PHEX BDCA4 Dentritic Cells PHF7 Testis Intersitial PHKG1 Superior Cervical Ganglion PHKG2 Testis PHLDA2 Placenta PHOX2A Uterus Corpus PI15 Testis Leydig Cell PI3 Tonsil PI4K2A CD71 Early Erythroid PIAS2 Testis Intersitial PIAS3 pineal day PIAS4 Whole Brain PIBF1 Testis Intersitial PICK1 Cerebellum Peduncles PIGB X721 B lymphoblasts PIGL Colorectal adenocarcinoma PIGR Trachea PIGV Testis PIGZ Pancreas PIK3C2B Thymus PIK3CA CD8 T cells PIK3R2 Fetal brain PIK3R5 CD56 NK Cells PIP5K1B CD71 Early Erythroid PIPOX Liver PIR Bronchial Epithelial Cells PITPNM3 Superior Cervical Ganglion PITX1 Tongue PITX2 retina PITX3 Adrenal gland PKD2 Uterus PKDREJ CD14 Monocytes PKLR Liver PKMYT1 CD71 Early Erythroid PKP2 Colon PLA1A X721 B lymphoblasts PLA2G12A CD105 Endothelial PLA2G2E Superior Cervical Ganglion PLA2G2F Trigeminal Ganglion PLA2G3 Skeletal Muscle PLA2G4A Smooth Muscle PLA2G7 CD14 Monocytes PLAA X721 B lymphoblasts PLAC1 Placenta PLAC4 Placenta PLAG1 Trigeminal Ganglion PLAGL2 Testis PLCB2 CD14 Monocytes PLCB3 small intestine PLCB4 Thalamus PLCXD1 X721 B lymphoblasts PLD1 X721 B lymphoblasts PLEK2 Bronchial Epithelial Cells PLEKHA2 Superior Cervical Ganglion PLEKHA6 Placenta PLEKHA8 CD56 NK Cells PLEKHF2 CD19 Bcells neg. sel. PLEKHH3 Superior Cervical Ganglion PLK1 X721 B lymphoblasts PLK3 CD33 Myeloid PLK4 CD71 Early Erythroid PLN Uterus PLOD2 Smooth Muscle PLS1 Colon PLSCR2 Testis Intersitial PLUNC Trachea PLXNA1 Fetal brain PLXNC1 Whole Blood PMCH Hypothalamus PMCHL1 Hypothalamus PMEPA1 Prostate PNMT Adrenal Cortex PNPLA2 Adipocyte PNPLA3 Atrioventricular Node PNPLA4 Bronchial Epithelial Cells POF1B Skin POFUT2 Smooth Muscle POLE2 Leukemia lymphoblastic MOLT 25 POLL CD71 Early Erythroid POLM CD19 Bcells neg. sel. POLQ Lymphoma burkitts Daudi POLR1C Leukemia promyelocytic HL65 POLR2D Testis POLR2J Trigeminal Ganglion POLR3B X721 B lymphoblasts POLR3C CD71 Early Erythroid POLR3D X721 B lymphoblasts POLR3G Leukemia promyelocytic HL66 POLRMT Testis POM121L2 Superior Cervical Ganglion POMC Pituitary POMGNT1 Heart POMT1 Testis POMZP3 Testis Germ Cell PON3 Liver POP1 Dorsal Root Ganglion POPDC2 Heart POSTH Cardiac Myocytes POU2F3 Trigeminal Ganglion POU3F3 Superior Cervical Ganglion POU3F4 Ciliary Ganglion POU4F2 Superior Cervical Ganglion POU5F1 Pituitary POU5F1P3 Uterus Corpus POU5F1P4 Ciliary Ganglion PP14571 Placenta PPA1 Heart PPARD Placenta PPARG Adipocyte PPARGC1A Salivary gland PPAT X721 B lymphoblasts PPBPL2 Superior Cervical Ganglion PPCDC X721 B lymphoblasts PPEF2 retina PPFIA2 pineal day PPFIBP1 Colorectal adenocarcinoma PPIL2 Leukemia chronic Myelogenous K588 PPIL6 Liver PPM1D CD51 PPM1H Cerebellum PPOX CD71 Early Erythroid PPP1R12B Uterus PPP1R13B Thyroid PPP1R3D Whole Blood PPP2R2D Whole Brain PPP3R1 Whole Blood PPP5C X721 B lymphoblasts PPRC1 CD105 Endothelial PPT2 Olfactory Bulb PPY Pancreatic Islet PPY2 Superior Cervical Ganglion PQLC2 Skeletal Muscle PRAME Leukemia chronic Myelogenous K589 PRDM1 Superior Cervical Ganglion PRDM11 CD52 PRDM12 Cardiac Myocytes PRDM13 Superior Cervical Ganglion PRDM16 Superior Cervical Ganglion PRDM5 Skeletal Muscle PRDM8 Superior Cervical Ganglion PREP X721 B lymphoblasts PRF1 CD56 NK Cells PRG3 Bone marrow PRICKLE3 X721 B lymphoblasts PRKAA1 Testis Intersitial PRKAB1 CD71 Early Erythroid PRKAB2 Dorsal Root Ganglion PRKCG Superior Cervical Ganglion PRKCH CD56 NK Cells PRKRIP1 Colorectal adenocarcinoma PRKY CD4 T cells PRL Pituitary PRLH Trigeminal Ganglion PRM2 Testis Leydig Cell PRMT3 Leukemia promyelocytic HL67 PRMT7 BDCA4 Dentritic Cells PRND Testis Germ Cell PRO1768 Trigeminal Ganglion PRO2012 Appendix PROC Liver PROCR Placenta PROL1 Salivary gland PROP1 Trigeminal Ganglion PROZ Superior Cervical Ganglion PRPS2 Ovary PRR3 Leukemia lymphoblastic MOLT 26 PRR5 CD71 Early Erythroid PRR7 X721 B lymphoblasts PRRC1 BDCA4 Dentritic Cells PRRG1 Spinal Cord PRRG2 Parietal Lobe PRRG3 Salivary gland PRRX1 Adipocyte PRSS12 Superior Cervical Ganglion PRSS16 Thymus PRSS21 Testis PRSS8 Placenta PSCA Prostate PSD Subthalamic Nucleus PSG1 Placenta PSG11 Placenta PSG2 Placenta PSG3 Placenta PSG4 Placenta PSG5 Placenta PSG6 Placenta PSG7 Placenta PSG9 Placenta PSKH1 Testis PSMB4 Superior Cervical Ganglion PSMD5 Leukemia chronic Myelogenous K590 PSPH Lymphoma burkitts Raji PSPN Trigeminal Ganglion PSTPIP2 Bone marrow PTCH2 Fetal brain PTDSS2 Lymphoma burkitts Raji PTER Kidney PTGDR CD56 NK Cells PTGER2 CD56 NK Cells PTGES2 X721 B lymphoblasts PTGES3 Superior Cervical Ganglion PTGFR Uterus PTGIR CD14 Monocytes PTGS1 Smooth Muscle PTGS2 Smooth Muscle PTH2R Superior Cervical Ganglion PTHLH Bronchial Epithelial Cells PTK7 BDCA4 Dentritic Cells PTPLA CD53 PTPN1 CD19 Bcells neg. sel. PTPN21 Testis PTPN3 Thalamus PTPN9 Appendix PTPRG Adipocyte PTPRH Pancreas PTPRS BDCA4 Dentritic Cells PURG Skeletal Muscle PUS3 Skeletal Muscle PUS7L Superior Cervical Ganglion PVALB Cerebellum PVRL3 Placenta PXDN Smooth Muscle PXMP2 Liver PXMP4 Lung PYGM Skeletal Muscle PYGO1 Skeletal Muscle PYHIN1 Superior Cervical Ganglion PYY Colon PZP Skin QPRT Liver QRSL1 CD19 Bcells neg. sel. QTRT1 Thyroid RAB11B Thyroid RAB11FIP3 Kidney RAB17 Liver RAB23 Uterus RAB25 Tongue RAB30 Liver RAB33A Whole Brain RAB38 Bronchial Epithelial Cells RAB3D Atrioventricular Node RAB40A Dorsal Root Ganglion RAB40C Superior Cervical Ganglion RAB4B BDCA4 Dentritic Cells RABL2A Fetal brain RAC3 Whole Brain RAD51L1 Superior Cervical Ganglion RAD52 Lymphoma burkitts Raji RAD9A CD105 Endothelial RAG1 Thymus RALGPS1 Fetal brain RAMP1 Uterus RAMP2 Lung RAMP3 Lung RANBP10 CD71 Early Erythroid RANBP17 Colorectal adenocarcinoma RAP2C Uterus RAPGEF1 Uterus Corpus RAPGEF4 Amygdala RAPGEFL1 Whole Brain RAPSN Skeletal Muscle RARA Whole Blood RARB Superior Cervical Ganglion RARS2 Uterus Corpus RASA1 Placenta RASA2 CD8 T cells RASA3 CD56 NK Cells RASAL1 Lymphoma burkitts Raji RASGRF1 Cerebellum RASGRP3 CD19 Bcells neg. sel. RASSF7 Pancreas RASSF8 Testis Intersitial RASSF9 Appendix RAVER2 Ciliary Ganglion RAX Cerebellum Peduncles RBBP5 CD14 Monocytes RBM19 Superior Cervical Ganglion RBM4B Fetal brain RBM7 Whole Blood RBMY1A1 Testis RBP4 Liver RBPJL Pancreas RBX1 CD71 Early Erythroid RC3H2 BDCA4 Dentritic Cells RCAN3 Prostate RCBTB2 Leukemia lymphoblastic MOLT 27 RCN3 Smooth Muscle RDH11 Prostate RDH16 Liver RDH8 retina RECQL4 CD105 Endothelial RECQL5 Skeletal Muscle RELB Lymphoma burkitts Raji REN Ovary RENBP Kidney RERGL Uterus RETSAT Adipocyte REV3L Uterus REXO4 CD19 Bcells neg. sel. RFC1 Leukemia lymphoblastic MOLT 28 RFC2 X721 B lymphoblasts RFNG Liver RFPL3 Superior Cervical Ganglion RFWD3 CD105 Endothelial RFX1 Superior Cervical Ganglion RFX3 Trigeminal Ganglion RFXAP Pituitary RGN Adrenal gland RGPD5 Testis Intersitial RGR retina RGS14 Caudate nucleus RGS17 Pancreatic Islet RGS3 Heart RGS6 pineal night RG59 Caudate nucleus RHAG CD71 Early Erythroid RHBDF1 Olfactory Bulb RHBDL1 Lymphoma burkitts Raji RHBG Atrioventricular Node RHCE CD71 Early Erythroid RHD CD71 Early Erythroid RHO retina RHOBTB1 Placenta RHOBTB2 Lung RHOD Bronchial Epithelial Cells RIBC2 Testis Intersitial RIC3 Cingulate Cortex RIC8B Caudate nucleus RIN3 CD14 Monocytes RINT1 Superior Cervical Ganglion RIOK2 Smooth Muscle RIT1 Whole Blood RIT2 Fetal brain RLBP1 retina RLN1 Prostate RLN2 Superior Cervical Ganglion RMI1 X721 B lymphoblasts RMND1 Trigeminal Ganglion RMND5A CD71 Early Erythroid RMND5B Testis RNASE3 Bone marrow RNASEH2B Leukemia lymphoblastic MOLT 29 RNASEL Whole Blood RNF10 CD71 Early Erythroid RNF121 Subthalamic Nucleus RNF123 CD71 Early Erythroid RNF125 CD8 T cells RNF14 CD71 Early Erythroid RNF141 Testis Intersitial RNF17 Testis Intersitial RNF170 Thyroid RNF185 Superior Cervical Ganglion RNF19A CD71 Early Erythroid RNF32 Testis Intersitial RNF40 CD71 Early Erythroid RNFT1 Testis Leydig Cell RNMTL1 Testis ROBO1 Fetal brain ROPN1 Testis Intersitial ROR1 Adipocyte RORB Superior Cervical Ganglion RORC Liver RP2 Whole Blood RPA4 Superior Cervical Ganglion RPAIN Lymphoma burkitts Daudi RPE Leukemia promyelocytic HL68 RPE65 retina RPGRIP1 Testis Intersitial RPGRIP1L Superior Cervical Ganglion RPH3AL Pancreatic Islet RPL10L Testis RPL3L Skeletal Muscle RPP38 Testis Germ Cell RPRM Fetal brain RPS6KA4 Pons RPS6KA6 Appendix RPS6KB1 CD4 T cells RPS6KC1 Testis Intersitial RRAD Skeletal Muscle RRAGB Superior Cervical Ganglion RRH retina RRH3 CD56 NK Cells RRP12 CD33 Myeloid RRP9 X721 B lymphoblasts RS1 retina RSAD2 CD71 Early Erythroid RSF1 Uterus RTDR1 Testis RTN2 Skeletal Muscle RUNX1T1 Fetal brain RUNX2 Pons RWDD2A Testis Germ Cell RXFP3 Superior Cervical Ganglion RYR2 Prefrontal Cortex S100A12 Bone marrow S100A2 Bronchial Epithelial Cells S100A3 Colorectal adenocarcinoma S100A5 Liver S100G Uterus Corpus S1PR5 CD56 NK Cells SAA1 Salivary gland SAA3P Skin SAA4 Liver SAC3D1 Testis SAG retina SAMHD1 CD33 Myeloid SAMSN1 Leukemia chronic Myelogenous K591 SAR1B small intestine SARDH Liver SATB2 Fetal brain SBNO1 Appendix SCAMP3 Atrioventricular Node SCAND2 Superior Cervical Ganglion SCAPER Fetal brain SCARA3 Uterus Corpus SCGB1D2 Skin SCGB2A2 Skin SCGN Pancreatic Islet SCIN Trigeminal Ganglion SCLY Liver SCN3A Fetal brain SCN4A Skeletal Muscle SCN5A Heart SCN8A Superior Cervical Ganglion SCNN1B Lung SCNN1D Superior Cervical Ganglion SCO2 CD33 Myeloid SCRIB Heart SCRT1 Superior Cervical Ganglion SCT BDCA4 Dentritic Cells SCUBE3 Superior Cervical Ganglion SCYL2 BDCA4 Dentritic Cells SCYL3 BDCA4 Dentritic Cells SDCCAG3 Lymphoma burkitts Raji SDF2 Whole Blood SDPR Fetal lung SDS Liver SEC14L3 Trigeminal Ganglion SEC14L4 CD71 Early Erythroid SEC22B Placenta SECTM1 Whole Blood SEL1L Pancreas SELE retina SELP Whole Blood SEMA3A Appendix SEMA3B Placenta SEMA3D Trigeminal Ganglion SEMA4G Fetal liver SEMA5A Olfactory Bulb SEMA7A Superior Cervical Ganglion SEMG1 Prostate SEMG2 Prostate SENP2 Testis Intersitial SEPHS1 Leukemia lymphoblastic MOLT 30 SERPINA10 Liver SERPINA7 Fetal liver SERPINB13 Tongue SERPINB3 Trachea SERRINB4 Superior Cervical Ganglion SERPINB8 CD33 Myeloid SERPINE1 Cardiac Myocytes SERPINF2 Liver SETD4 Testis SETD8 CD71 Early Erythroid SETMAR Atrioventricular Node SF3A3 Leukemia chronic Myelogenous K592 SFMBT1 Testis Germ Cell SFRP5 retina SFTPA2 Lung SFTPD Lung SGCA Heart SGCB Olfactory Bulb SGPL1 Colorectal adenocarcinoma SGPP1 Placenta SGTA Heart SH2D1A Leukemia lymphoblastic MOLT 31 SH2D3C Thymus SH3BGR Skeletal Muscle SH3TC1 Thymus SH3TC2 Placenta SHANK1 CD56 NK Cells SHC2 Pancreatic Islet SHC3 Prefrontal Cortex SHH Superior Cervical Ganglion SHGX2 Thalamus SHQ1 Leukemia lymphoblastic MOLT 32 SHROOM2 pineal night SI small intestine SIAH1 Placenta SIAH2 CD71 Early Erythroid SIGLEC1 Lymph node SIGLEC5 Superior Cervical Ganglion SIGLEC6 Placenta SILV retina SIM1 Superior Cervical Ganglion SIM2 Skeletal Muscle SIRPB1 Whole Blood SIRT1 CD19 Bcells neg. sel. SIRT4 Superior Cervical Ganglion SIRT5 Heart SIRT7 CD33 Myeloid SIX1 Pituitary SIX2 Pituitary SIX3 retina SIX5 Superior Cervical Ganglion SKAP1 CD8 T cells SLAMF1 X721 B lymphoblasts SLC10A1 Liver SLC10A2 small intestine SLC12A1 Kidney SLC12A2 Trachea SLC12A6 Testis Intersitial SLC12A9 CD14 Monocytes SLC13A2 Kidney SLC13A3 Kidney SLC13A4 pineal night SLC14A1 CD71 Early Erythroid SLC15A1 Superior Cervical Ganglion SLC16A10 Superior Cervical Ganglion SLC16A4 Placenta SLC16A8 retina SLC17A1 Superior Cervical Ganglion SLC17A3 Kidney SLC17A4 Superior Cervical Ganglion SLC17A5 Placenta SLC18A1 Skeletal Muscle SLC18A2 Uterus SLC19A2 Adrenal Cortex SLC19A3 Placenta SLC1A5 Colorectal adenocarcinoma SLC1A6 Cerebellum SLC1A7 Trigeminal Ganglion SLC20A2 Thyroid SLC22A1 Liver SLC22A13 Superior Cervical Ganglion SLC22A18AS Lymphoma burkitts Raji SLC22A2 Kidney SLC22A3 Prostate SLC22A4 CD71 Early Erythroid SLC22A6 Kidney SLC22A7 Liver SLC22A8 Kidney SLC24A1 retina SLC24A2 Ciliary Ganglion SLC24A6 Adrenal gland SLC25A10 Liver SLC25A11 Heart SLC25A17 X721 B lymphoblasts SLC25A21 Leukemia chronic Myelogenous K593 SLC25A28 BDCA4 Dentritic Cells SLC25A31 Testis SLC25A37 Bone marrow SLC25A38 CD71 Early Erythroid SLC25A4 Skeletal Muscle SLC25A42 Superior Cervical Ganglion SLC26A2 Colon SLC26A3 Colon SLC26A4 Thyroid SLC26A6 Leukemia lymphoblastic MOLT 33 SLC27A2 Kidney SLC27A5 Liver SLC27A6 Olfactory Bulb SLC28A3 Pons SLC29A1 CD71 Early Erythroid SLC2A11 pineal day SLC2A14 Colorectal adenocarcinoma SLC2A2 Fetal liver SLC2A6 CD14 Monocytes SLC30A10 Fetal liver SLC31A1 CD105 Endothelial SLC33A1 BDCA4 Dentritic Cells SLC34A1 Kidney SLC35A3 Colon SLC35C1 Colorectal adenocarcinoma SLC35E3 Prostate SLC37A1 X721 B lymphoblasts SLC37A4 Liver SLC38A3 Liver SLC38A4 Fetal liver SLC38A6 CD105 Endothelial SLC38A7 Prefrontal Cortex SLC39A7 Prostate SLC3A1 Kidney SLC41A3 Testis SLC45A2 retina SLC47A1 Adrenal Cortex SLC4A1 CD71 Early Erythroid SLC4A3 Heart SLC5A1 small intestine SLC5A2 Kidney SLCSA4 Superior Cervical Ganglion SLC5A5 Thyroid SLC5A6 Placenta SLC6A11 Skeletal Muscle SLC6A12 Kidney SLC6A14 Fetal lung SLC6A15 Bronchial Epithelial Cells SLC6A20 Trigeminal Ganglion SLC6A4 pineal night SLC6A7 Superior Cervical Ganglion SLC6A9 CD71 Early Erythroid SLC9A1 Placenta SLC9A3 Superior Cervical Ganglion SLC9A5 Prefrontal Cortex SLC9A8 CD33 Myeloid SLCO2B1 Liver SLCO4C1 Ciliary Ganglion SLCO5A1 X721 B lymphoblasts SLFN12 CD33 Myeloid SLIT1 Leukemia lymphoblastic MOLT 34 SLIT3 Adipocyte SLITRK3 Subthalamic Nucleus SLMO1 Superior Cervical Ganglion SLURP1 Tongue SMC2 Leukemia lymphoblastic MOLT 35 SMCHD1 Whole Blood SMCP Testis Intersitial SMG6 Appendix SMR3A Salivary gland SMR3B Salivary gland SMURF1 Testis SMYD3 Leukemia chronic Myelogenous K594 SMYD5 Pancreas SNAPC1 Testis Intersitial SNAPC4 Testis SNCAIP Uterus Corpus SNIP1 Globus Pallidus SNX1 Fetal Thyroid SNX16 Trigeminal Ganglion SNX19 Superior Cervical Ganglion SNX2 CD19 Bcells neg. sel. SNX24 Spinal Cord SOAT1 Adrenal gland SOAT2 Fetal liver SOCS1 Lymphoma burkitts Raji SOCS2 Leukemia chronic Myelogenous K595 SOCS6 Colon SOD3 Thyroid SOHLH2 X721 B lymphoblasts SOS1 Adipocyte SOSTDC1 retina SOX1 Superior Cervical Ganglion SOX11 Fetal brain SOX12 Fetal brain SOX18 Superior Cervical Ganglion SOX5 Testis Intersitial SP140 CD19 Bcells neg. sel. SPA17 Testis Intersitial SPAG1 Appendix SPAG11B Testis Leydig Cell SPAG6 Testis SPANXB1 Testis Seminiferous Tubule SPAST Fetal brain SPATA2 Testis SPATA5L1 Leukemia promyelocytic HL69 SPATA6 Testis Intersitial SPC25 Leukemia chronic Myelogenous K596 SPCS3 BDCA4 Dentritic Cells SPDEF Prostate SPEG Uterus SPIB Lymphoma burkitts Raji SPINT3 Testis Germ Cell SPO11 Trigeminal Ganglion SPPL2B CD54 SPR Liver SPRED2 Thymus SRD5A1 Fetal brain SRD5A2 Liver SREBF1 Adrenal Cortex SRF CD71 Early Erythroid SRR Superior Cervical Ganglion SSH3 Bronchial Epithelial Cells SSR3 Prostate SSSCA1 CD105 Endothelial SST Pancreatic Islet SSTR1 Atrioventricular Node SSTR4 Ciliary Ganglion SSTR5 Subthalamic Nucleus SSX2 Superior Cervical Ganglion SSX5 Liver ST3GAL1 CD8 T cells ST6GALNAC4 CD71 Early Erythroid ST7 X721 B lymphoblasts ST7L Ovary ST8SIA2 Superior Cervical Ganglion ST8SIA4 Whole Blood ST8SLA5 Adrenal gland STAB2 Lymph node STAC Ciliary Ganglion STAG3L4 Appendix STAM2 Testis Intersitial STARD13 X721 B lymphoblasts STARD5 Uterus Corpus STAT2 BDCA4 Dentritic Cells STAT5A Leukemia lymphoblastic MOLT 36 STBD1 Pancreatic Islet STC1 Smooth Muscle STEAP1 Prostate STEAP3 CD71 Early Erythroid STIL Trigeminal Ganglion STK11 CD71 Early Erythroid STK16 X721 B lymphoblasts STMN3 Amygdala STON1 Uterus STRN Ciliary Ganglion STRN3 Uterus STS Placenta STX17 Superior Cervical Ganglion STX2 CD8 T cells STX3 Whole Blood STX6 Whole Blood STYK1 Trigeminal Ganglion SUCLG1 Kidney SULT1A3 Ciliary Ganglion SULT2A1 Adrenal gland SULT2B1 Tongue SUOX Liver SUPT3H Testis Seminiferous Tubule SUPV3L1 Leukemia promyelocytic HL70 SURF2 Testis Germ Cell SUV39H1 CD71 Early Erythroid SVEP1 Placenta SYCP1 Testis Intersitial SYCP2 Testis Leydig Cell SYDE1 Placenta SYF2 Skeletal Muscle SYN3 Skeletal Muscle SYNGR4 Testis SYNPO2L Heart SYP pineal night SYT12 Trigeminal Ganglion T X721 B lymphoblasts TAAR3 Superior Cervical Ganglion TAAR5 Superior Cervical Ganglion TAC1 Caudate nucleus TAC3 Placenta TACR3 Pancreas TAF4 Leukemia lymphoblastic MOLT 37 TAF5L CD71 Early Erythroid TAF7L Testis Germ Cell TAL1 CD71 Early Erythroid TANC2 Superior Cervical Ganglion TAP2 CD56 NK Cells TARBP1 CD55 TAS2R1 Globus Pallidus TAS2R14 Superior Cervical Ganglion TAS2R7 Superior Cervical Ganglion TAS2R9 Subthalamic Nucleus TASP1 Superior Cervical Ganglion TAT Liver TBC1D12 Spinal Cord TBC1D13 Kidney TBC1D16 Adipocyte TBC1D22A CD19 Bcells neg. sel. TBC1D22B CD71 Early Erythroid TBC1D29 Dorsal Root Ganglion TBC1D8B Pituitary TBCA Superior Cervical Ganglion TBCD Leukemia lymphoblastic MOLT 38 TBCE CD56 TBL1Y Superior Cervical Ganglion TBL2 Testis TBP Testis Intersitial TBRG4 Lymphoma burkitts Raji TBX10 Skeletal Muscle TBX19 Pituitary TBX21 CD56 NK Cells TBX3 Adrenal gland TBX4 Temporal Lobe TBX5 Superior Cervical Ganglion TCHH Placenta TCL1B Atrioventricular Node TCL6 Cardiac Myocytes TCN2 Kidney TCP11 Testis Intersitial TDP1 Testis Intersitial TEAD3 Placenta TEAD4 Colorectal adenocarcinoma TEC Liver TECTA Superior Cervical Ganglion TESK2 CD19 Bcells neg. sel. TEX13B Skeletal Muscle TEX14 Testis Seminiferous Tubule TEX15 Testis Seminiferous Tubule TEX28 Testis TFAP2A Placenta TFAP2B Skeletal Muscle TFAP2C Placenta TFB1M Leukemia promyelocytic HL71 TFB2M Leukemia chronic Myelogenous K597 TFCP2L1 Salivary gland TFDP1 CD71 Early Erythroid TFDP3 Superior Cervical Ganglion TFEC CD33 Myeloid TFF3 Pancreas TFR2 Liver TGDS Pancreas TGFB1I1 Uterus TGM2 Placenta TGM3 Tongue TGM4 Prostate TGM5 Liver TGS1 CD105 Endothelial THADA CD4 T cells THAP10 Whole Brain THAP3 Lymphoma burkitts Raji THBS3 Testis THG1L CD105 Endothelial THNSL2 Liver THRB Superior Cervical Ganglion THSD1 Pancreas THSD4 Superior Cervical Ganglion THSD7A Placenta THUMPD2 Leukemia lymphoblastic MOLT 39 TIMM22 Whole Brain TIMM50 Skin TIMM88 Heart TIMP2 Placenta TLE3 Whole Blood TLE6 CD71 Early Erythroid TLL1 Superior Cervical Ganglion TLL2 Heart TLR3 Testis Intersitial TLR7 BDCA4 Dentritic Cells TLX3 Cardiac Myocytes TM4SF20 small intestine TM4SF5 Liver TM7SF2 Adrenal gland TMCC1 Pancreas TMCC2 CD71 Early Erythroid TMCO3 Smooth Muscle TMEM104 Skin TMEM11 CD71 Early Erythroid TMEM110 Liver TMEM121 CD14 Monocytes TMEM135 Adipocyte TMEM140 Whole Blood TMEM149 BDCA4 Dentritic Cells TMEM159 Heart TMEM186 X721 B lymphoblasts TMEM187 Lung TMEM19 Superior Cervical Ganglion TMEM2 Placenta TMEM209 Superior Cervical Ganglion TMEM39A Pituitary TMEM45A Skin TMEM48 X721 B lymphoblasts TMEM53 Liver TMEM57 CD71 Early Erythroid TMEM62 Cingulate Cortex TMEM63A GD4 T cells TMEM70 Skeletal Muscle TMLHE Superior Cervical Ganglion TMPRSS2 Prostate TMPRSS3 small intestine TMPRSS5 Olfactory Bulb TMPRSS6 Liver TNFAIP6 Smooth Muscle TNRSF10C Whole Blood TNFRSF10D Cardiac Myocytes TNFRSF11A Appendix TNFRSF11B Thyroid TNFRSF14 Lymphoma burkitts Raji TNFRSF25 CD4 T cells TNFRSF4 Lymph node TNFRSF8 X721 B lymphoblasts TNFRSF9 Ciliary Ganglion TNFSF11 Lymph node TNFSF14 X721 B lymphoblasts TNFSF8 CD4 T cells TNFSF9 Leukemia promyelocytic HL72 TNIP2 Lymphoma burkitts Raji TNN pineal night TNNI1 Skeletal Muscle TNNI3 Heart TNNI3K Superior Cervical Ganglion TNNT1 Skeletal Muscle TNNT2 Heart TNP1 Testis Intersitial TNP2 Testis Intersitial TNR Skeletal Muscle TNS4 Colorectal adenocarcinoma TNXA Adrenal Cortex TNXB Adrenal Cortex TOM1L1 Bronchial Epithelial Cells TOMM22 X721 B lymphoblasts TOP3B Leukemia chronic Myelogenous K598 TOX3 Colon TOX4 Superior Cervical Ganglion TP53BP1 pineal night TP73 Skeletal Muscle TPPP3 Placenta TPSAB1 Lung TRABD BDCA4 Dentritic Cells TRADD CD4 T cells TRAF1 X721 B lymphoblasts TRAF2 Lymphoma burkitts Raji TRAF3IP2 Bronchial Epithelial Cells TRAF6 Leukemia chronic Myelogenous K599 TRAK1 CD19 Bcells neg. sel. TRAK2 CD71 Early Erythroid TRDMT1 Superior Cervical Ganglion TRDN Tongue TREH Kidney TREML2 Placenta TRH Hypothalamus TRIM10 CD71 Early Erythroid TRIM13 Testis Intersitial TRIM15 Pancreas TRIM17 Ciliary Ganglion TRIM21 Whole Blood TRIM23 Amygdala TRIM25 Placenta TRIM29 Tongue TRIM31 Skeletal Muscle TRIM32 Cerebellum TRIM36 Amygdala TRIM46 CD71 Early Erythroid TRIM68 CD56 NK Cells TRIO Fetal brain TRIP10 Skeletal Muscle TRIP11 Testis Intersitial TRMT12 CD105 Endothelial TRMU CD8 T cells TRPA1 Superior Cervical Ganglion TRPC5 Superior Cervical Ganglion TRPM1 retina TRPM2 BDCA4 Dentritic Cells TRPM8 Skeletal Muscle TRPV4 Superior Cervical Ganglion TRRAP Leukemia lymphoblastic MOLT 40 TSGA10 Testis Intersitial TSHB Pituitary TSKS Testis Intersitial TSPAN1 Trachea TSPAN15 Olfactory Bulb TSPAN32 CD8 T cells TSPAN5 CD71 Early Erythroid TSPAN9 Heart TSSC4 Heart TSTA3 CD105 Endothelial TTC15 Testis Intersitial TTC22 Superior Cervical Ganglion TTC23 Lymphoma burkitts Raji TTC27 Leukemia chronic Myelogenous K600 TTC28 Fetal brain TTC9 Fetal brain TTLL12 CD105 Endothelial TTLL4 Testis TTLL5 Testis Intersitial TTPA Atrioventricular Node TTTY9A Superior Cervical Ganglion TUBA4B Lymphoma burkitts Raji TUBA8 Superior Cervical Ganglion TUBAL3 small intestine TUBB4Q Skeletal Muscle TUBD1 Superior Cervical Ganglion TUFM Superior Cervical Ganglion TUFT1 Skin TWSG1 Smooth Muscle TYR retina TYRP1 retina U2AF1 Superior Cervical Ganglion UAP1L1 X721 B lymphoblasts UBA1 Superior Cervical Ganglion UBE2D1 Whole Blood UBE2D4 Liver UBFD1 CD105 Endothelial UBQLN3 Testis Intersitial UCN pineal night UCP1 Fetal Thyroid UFC1 Trigeminal Ganglion UGT2A1 Atrioventricular Node UGT2B15 Liver UGT2B17 Appendix ULBP1 Cerebellum ULBP2 Bronchial Epithelial Cells UMOD Kidney UNC119 Lymphoma burkitts Raji UNC5C Superior Cervical Ganglion UNC93A Fetal liver UNC93B1 BDCA4 Dentritic Cells UPB1 Liver UPF1 Prostate UPK1A Prostate UPK1B Trachea UPK3A Prostate UPK3B Lung UPP1 Bronchial Epithelial Cells UQCC Lymphoma burkitts Raji UQCRC1 Heart UQCRFS1 Superior Cervical Ganglion URM1 Heart UROD CD71 Early Erythroid USH2A pineal day USP10 Whole Blood USP12 CD71 Early Erythroid USP13 Skeletal Muscle USP13 X721 B lymphoblasts USP19 Trigeminal Ganglion USP2 Testis Germ Cell USP27X Superior Cervical Ganglion USP29 Superior Cervical Ganglion USP32 Testis Intersitial USP6NL Atrioventricular Node UTRN Testis Intersitial UTS2 CD56 NK Cells UTY Ciliary Ganglion UVRAG CD19 Bcells neg. sel. VAC14 Skeletal Muscle VARS X721 B lymphoblasts VASH1 pineal night VASH2 Fetal brain VASP Whole Blood VAV2 CD19 Bcells neg. sel. VAV3 Placenta VAX2 Superior Cervical Ganglion VCPIP1 CD33 Myeloid VENTX CD33 Myeloid VGF Pancreatic Islet VGLL1 Placenta VGLL3 Placenta VILL Colon VIPR1 Lung VLDLR Pancreatic Islet VNN2 Whole Blood VNN3 CD33 Myeloid VPRBP Testis Intersitial VPREB1 CD57 VPS13B CD8 T cells VPS33B Testis VPS45 pineal day VPS53 Skin VSIG4 Lung VSX1 Superior Cervical Ganglion VTCN1 Trachea WARS2 X721 B lymphoblasts WASL Colon WDR18 X721 B lymphoblasts WDR25 Lung WDR43 Lymphoma burkitts Daudi WDR55 CD4 T cells WDR58 Superior Cervical Ganglion WDR60 Testis Intersitial WDR67 CD56 NK Cells WDR70 BDCA4 Dentritic Cells WDR78 Testis Seminiferous Tubule WDR8 Lymphoma burkitts Raji WDR91 X721 B lymphoblasts WHSC1L1 Ovary WHSC2 Lymphoma burkitts Raji WIPI1 CD71 Early Erythroid WISP1 Uterus Corpus WISP3 Superior Cervical Ganglion WNT11 Uterus Corpus WNT2B retina WNT3 Superior Cervical Ganglion WNT4 Pancreatic Islet WNT5A Colorectal adenocarcinoma WNT5B Prostate WNT6 Colorectal adenocarcinoma WNT7A Bronchial Epithelial Cells WNT7B Skeletal Muscle WNT8B Skin WRNIP1 Trigeminal Ganglion WT1 Uterus WWC3 CD19 Bcells neg. sel. XCL1 CD56 NK Cells XK CD71 Early Erythroid XPNPEP2 Kidney XPO4 pineal day XPO6 Whole Blood XPO7 CD71 Early Erythroid XRCC3 Colorectal adenocarcinoma YAF2 Skeletal Muscle YBX2 Testis YIF1A Liver YIPF6 CD71 Early Erythroid YWHAQ Skeletal Muscle YY2 Uterus Corpus ZAK Dorsal Root Ganglion ZAP70 CD56 NK Cells ZBED4 Dorsal Root Ganglion ZBTB10 Superior Cervical Ganglion ZBTB17 Lymphoma burkitts Raji ZBTB24 Skin ZBTB3 Superior Cervical Ganglion ZBTB33 Superior Cervical Ganglion ZBTB40 CD4 T cells ZBTB43 CD33 Myeloid ZBTB5 CD19 Bcells neg. sel. ZBTB6 Superior Cervical Ganglion ZBTB7B Ovary ZC3H12A Smooth Muscle ZC3H14 Testis Intersitial ZCCHC2 Salivary gland ZCWPW1 Testis Germ Cell ZDHHC13 X721 B lymphoblasts ZDHHC14 Lymphoma burkitts Raji ZDHHC18 Whole Blood ZDHHC3 Testis Intersitial ZER1 CD71 Early Erythroid ZFHX4 Smooth Muscle ZFP2 Superior Cervical Ganglion ZFP30 Ciliary Ganglion ZFPM2 Cerebellum ZFR2 Trigeminal Ganglion ZFYVE9 Cingulate Cortex ZG16 Colon ZGPAT Liver ZIC3 Cerebellum ZKSCAN1 Pancreas ZKSCAN5 CD19 Bcells neg. sel. ZMAT5 Liver ZMYM1 Superior Cervical Ganglion ZMYND10 Testis ZNF124 Uterus Corpus ZNF132 Skin ZNF133 CD58 ZNF135 CD59 ZNF136 CD8 T cells ZNF14 Trigeminal Ganglion ZNF140 Superior Cervical Ganglion ZNF157 Trigeminal Ganglion ZNF167 Appendix ZNF175 Leukemia chronic Myelogenous K601 ZNF177 Testis Seminiferous Tubule ZNF185 Tongue ZNF193 Ovary ZNF200 Whole Blood ZNF208 Liver ZNF214 Superior Cervical Ganglion ZNF215 Dorsal Root Ganglion ZNF223 Ciliary Ganglion ZNF224 CD8 T cells ZNF226 pineal night ZNF23 CD71 Early Erythroid ZNF235 Superior Cervical Ganglion ZNF239 Testis Seminiferous Tubule ZNF250 Skin ZNF253 Superior Cervical Ganglion ZNF259 Testis ZNF264 CD4 T cells ZNF267 Whole Blood ZNF273 Skin ZNF274 CD19 Bcells neg. sel. ZNF280B Testis Intersitial ZNF286A Superior Cervical Ganglion ZNF304 Superior Cervical Ganglion ZNF318 X721 B lymphoblasts ZNF323 Superior Cervical Ganglion ZNF324 Thymus ZNF331 Adrenal Cortex ZNF34 Fetal Thyroid ZNF343 Ciliary Ganglion ZNF345 Superior Cervical Ganglion ZNF362 Atrioventricular Node ZNF3S5D Superior Cervical Ganglion ZNF391 Testis Intersitial ZNF415 Testis Intersitial ZNF430 CD8 T cells ZNF434 Globus Pallidus ZNF443 Trigeminal Ganglion ZNF446 Superior Cervical Ganglion ZNF45 CD60 ZNF451 CD71 Early Erythroid ZNF460 Trigeminal Ganglion ZNF467 Whole Blood ZNF468 CD56 NK Cells ZNF471 Skeletal Muscle ZNF484 Atrioventricular Node ZNF507 Fetal liver ZNF510 Appendix ZNF516 Uterus ZNF550 Temporal Lobe ZNF556 Ciliary Ganglion ZNF557 Ciliary Ganglion ZNF587 Superior Cervical Ganglion ZNF589 Superior Cervical Ganglion ZNF606 Fetal brain ZNF672 CD71 Early Erythroid ZNF636 Trigeminal Ganglion ZNF7 Skeletal Muscle ZNF711 Testis Germ Cell ZNF717 Appendix ZNF74 Dorsal Root Ganglion ZNF770 Skeletal Muscle ZNF771 Atrioventricular Node ZNF780A Superior Cervical Ganglion ZNF79 Leukemia lymphoblastic MOLT 41 ZNF8 Superior Cervical Ganglion ZNF80 Trigeminal Ganglion ZNF804A Lymphoma burkitts Daudi ZNF821 Testis Intersitial ZNHIT2 Testis ZP2 Cerebellum ZPBP Testis Intersitial ZSCAN16 CD19 Bcells neg. sel. ZSCAN2 Skeletal Muscle ZSWIM1 Ciliary Ganglion ZW10 Superior Cervical Ganglion ZXDB Ciliary Ganglion ZZZ3 CD61

The following table (Table 2) lists panel of 94 tissue-specific genes in Example 4 that were verified with qPCR.

TABLE 2 Panel of 94 tissue-specific genes in Example 4 that were verified with qPCR. Gene Tissue PMCH Amygdala HAPLN1 Bronchial epithelial cells PRDM12 Cardiac myocytes ARPP-21 Caudate nucleus GPR88 Caudate nucleus PDE10A Caudate nucleus CBLN1 Cerebellum CDH22 Cerebellum DGKG Cerebellum CDR1 Cerebellum FAT2 Cerebellum GABRA6 Cerebellum KCNJ12 Cerebellum KIAA0802 Cerebellum NEUROD1 Cerebellum NRXN3 Cerebellum PPFIA4 Cerebellum ZIC1 Cerebellum SAA4 Cervix SERPINC1 Cervix CALML4 Colon DSC2 Colon ACTC1 Heart NKX2-5 Heart CASQ2 Heart CKMT2 Heart HRC Heart HSPB3 Heart HSPB7 Heart ITGB1BP3 Heart MYL3 Heart MYL7 Heart MYOZ2 Heart NPPB Heart CSRP3 Heart MYBPC3 Heart PGAM2 Heart TNNI3 Heart SLC4A3 Heart TNNT2 Heart SYNPO2L Heart AVP Liver ACTB Housekeeping GAPDH Housekeeping MAB21L2 Housekeeping HCRT Hypothalamus OXT Hypothalamus BBOX1 Kidney AQP2 Kidney KCNJ1 Kidney FMO1 Kidney NAT8 Kidney XPNPEP2 Kidney PDZK1IP1 Kidney PTH1R Kidney SLC12A1 Kidney SLC13A3 Kidney SLC22A6 Kidney SLC22A8 Kidney SLC7A9 Kidney UMOD Kidney SLC17A3 Kidney AKR1C4 Liver C8G Liver APOF Liver AQP9 Liver CYP2A6 Liver CYP1A2 Liver CYP2C8 Liver CYP2D6 Liver CYP2E1 Liver ITIH4 Liver HRG Liver FTCD Liver IGFALS Liver RDH16 Liver SDS Liver SLC22A1 Liver TBX3 Liver SLC27A5 Liver KCNK12 Olfactory bulb MPZ Olfactory bulb C21ORF7 Whole blood FFAR2 Whole blood FCGR3A Whole blood EMR2 Whole blood FAM5B Whole blood FCGR3B Whole blood FPR2 Whole blood MLH3 Whole blood PF4 Whole blood PF4V1 Whole blood PPBP Whole blood TLR1 Whole blood TNFRSF10C Whole blood ZDHHC18 Whole blood

Example 5: Using Tissue-Specific Cell-Free RNA to Assess Alzheimer's

The analysis of fetal brain-specific transcripts, in Examples 2 and 3, leads to the assessment of brain-specific transcripts for neurological disorder. Particularly, the qPCR brain panel detected fetal brain-specific transcripts in maternal blood, whereas the whole transcriptome deconvolution analysis in our nonpregnant adult samples, in Examples 2 and 3, revealed that the hypothalamus is a significant contributor to the whole cell-free transcriptome. Since the hypothalamus is bounded by specialized brain regions that lack an effective blood-brain barrier, cell-free DNA in the blood was examined in the current study to measure neuronal death, qPCR was used to measure the expression levels of selected brain transcripts in the plasma of both Alzheimer's patients and age-matched normal controls. These measurements were made for a cohort of 16 patients: 6 diagnosed as Alzheimer's and 10 normal subjects. FIG. 17 depicts the measurements of PSD3 and APP cell-free RNA transcript levels in plasma. As provided in FIG. 17, the levels of PSD3 and APP cell-free RNA transcripts are elevated in Alzheimer's (AD) patients as compared to normal patients and can be used to characterize the different patient populations.

The APP transcript encodes for the precursor molecule whose proteolysis generates β amyloid, which is the primary component of amyloid plaques found in the brain of Alzheimer's disease patients. Preliminary measurements of the plasma APP transcript corroborate the known biology behind progression of Alzheimer's disease and showed a significant increase in patients with Alzheimer's disease compared with normal subjects, suggesting that plasma APP mRNA levels may be a good marker for diagnosing Alzheimer's disease. Similarly, the gene PSD3, which is highly expressed in the nervous system and localized to the postsynaptic density based on sequence similarities, shows an increase in the plasma of patients with Alzheimer's disease. By plotting the ΔCt values of APP against PSD3. AD patients were clustered away from the normal patients. In light of the cluster variants, cell-free RNA may serve as a blood-based diagnostic test for Alzheimer's disease and other neurodegenerative disorders.

Example 6: Assessing Neurological Disorders with Brain-Specific Transcripts

Overview

This study expands upon Example 5 and was designed to determine brain-specific tissue transcripts that correlate with the various stages of Alzheimer's disease. The study examined a cohort of patients from different centers that have previously collected Alzheimer's patents and age controlled references. There were a total of 254 plasma samples available from the different centers. Cell free RNA was extracted from each of the samples. The extracted cell free RNA from each of these samples were then assayed using high throughput qPCR on the Biomark Fluidigm system. Each of the samples was assayed using a panel of 48 genes of which 43 genes are known to be brain specific. The resulting measurements from each of the samples were put through a very stringent quality control process. The first step includes measuring the distribution of housekeeping genes: ACTB and GAPDH. By observing the levels of housekeeping genes across the sample from different batches, batches with significantly lower levels of housekeeping genes were removed from downstream analysis. The next step in quality control is by the number of failed gene assays in each of the patient sample. Sample where 8 or more assays failed to amplify are removed. This results in 125 good quality samples:

I. 27 Alzheimers Patients (AD)

II. 52 Mild Cognitive Impairment Patients (MCI)

III. 46 Normal patients.

IV.

Analysis and Results

An unsupervised method of Principle Component Analysis (PCA) was applied to the qPCR gene expression of the 43 brain-specific transcripts in order to differentiate between Alzheimer's and Normal patients. FIG. 27 illustrates the PCA space reflecting the unsupervised clustering of the patients using the gene expression data from the 48-gene assay. As shown in FIG. 27 two different populations are formed which correspond to the neurological disease state of the patients.

Additionally, a Wilcox non-parametric statistical test was performed between Alzheimer's and normal patients for each of the brain specific transcripts. The resulting p-values were bonferroni corrected for multiple testing. Brain specific transcripts whose p-values that are significant at the 0.05 levels were cataloged as transcripts that high distinguishing power between alzheimer's and normal patients. Amongst all the assayed brain specific transcripts, two of them are elevated in Alzheimer patients: APP and PSD3. Another 7 transcripts were below normal levels at a significant level: MOBP; MAG; SLC2A1; TCF7L2; CDH22: CNTF and PAQR6. FIG. 28 shows the boxplot of the different levels of APP transcripts across the different patient groups and the corrected P-value indicating the significance of the transcripts in distinguishing Alzheimer's. FIG. 29 illustrates the alternate trends where the levels of the measure brain transcript MOBP were lower in the Alzheimer population as compared to the normal population. MOBP is a myelin-associated oligodendrocyte protein-coding gene which is known to play a role in compacting or stabilizing the myelin sheath.

Methods of Normalization for Comparison Across Sample Batches

Considerable heterogeneity may be present between different batches of samples collected. A normalization scheme may be deployed to allow for valid comparison across samples from different batches, and such scheme was deployed in the present study. For each gene assay within each batch, the delta ct values of each sample was used to generate a z-score by using the mean and standard deviation inferred from the population of normal samples within the batch. This z-score is then used to as the normalized expression value for downstream analysis, as discussed below.

Classification Results Using Combined Z-Scores (See FIG. 30)

To incorporate the different measurements across the brain specific genes into a single distinct measure for classification of the patients, the method of combined z-scores was employed. The combined z-scores measure the deviation of the brain specific transcripts from the mean expected value of the normal controls and combine these deviations into a single measure for distinguishing Alzheimer's. To analyze the utility of such a measure in distinguishing Alzheimer's, a receiver-operator analysis was performed and achieved an area under curve (AUC) of 0.79 (See FIG. 30).

INCORPORATION BY REFERENCE

References and citations to other documents, such as patents, patent applications, patent publications, journals, books, papers, web contents, have been made throughout this disclosure. All such documents are hereby incorporated herein by reference in their entirety for all purposes.

EQUIVALENTS

The invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting on the invention described herein. Scope of the invention is thus indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. 

What is claimed is:
 1. A method for characterizing a neurological disorder of a patient, the method comprising: obtaining RNA from a blood sample of a patient suspected of having a neurological disorder, converting the RNA obtained in the sample into cDNA; determining a level of the sample cDNA that corresponds to RNA originating from brain tissue; comparing the level of the sample cDNA to a reference level of circulating RNA originating from brain tissue; and indicating a neurological disorder based upon a statistically-significant deviation between the level of sample cDNA and the reference level.
 2. The method of claim 1, further comprising the step of determining a stage of the indicated neurological disorder.
 3. The method of claim 2, wherein the stage is selected from the group consisting of no cognitive impairment, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment.
 4. The method of claim 1, wherein the neurological disorder is Alzheimer's disease.
 5. The method of claim 1, wherein the level of the sample cDNA and the reference level correspond to an amount of circulating RNA released from brain tissue selected from the group consisting of spinal cord, pituitary, hypothalamus, thalamus, corpus callosum, cerebrum, cerebral cortex, and combinations thereof.
 6. The method of claim 1, further comprising the step of monitoring progression of the neurological disorder by repeating the steps of obtaining through comparing.
 7. The method of claim 1, wherein the reference level comprises a level of cDNA corresponding to a patient population without cognitive impairment.
 8. The method of claim 1, wherein the reference level comprises a level of cDNA corresponding to a patient population diagnosed with a neurological disorder.
 9. The method of claim 1, wherein the blood sample is plasma or serum.
 10. The method of claim 1, wherein the determining step is performed via a sequencing technique, a microarray technique, or both.
 11. A method for characterizing a neurological disorder of a patient, the method comprising: obtaining RNA from a blood sample of a patient suspected of having a neurological disorder, converting the RNA obtained in the sample into cDNA; determining a level of the sample cDNA that corresponds to RNA originating from brain tissue; comparing the level of the sample cDNA to a set of variables correlated with a neurological disorder, wherein the variables comprise reference levels of cDNA that correspond to circulating RNA originating from brain tissue and to one or more stages of the neurological disorder; and indicating a stage of a neurological disorder of the patient based upon a statistically significant deviation between the level of the sample cDNA and the set of variables correlated with a neurological disorder.
 12. The method of claim 11, wherein the reference levels of cDNA further correspond to patient populations of certain ages.
 13. The method of claim 11, wherein the level of the sample cDNA and reference levels of cDNA correspond to an amount of circulating RNA released from brain tissue that is selected from the group consisting of pituitary, hypothalamus, thalamus, corpus callosum, cerebrum, cerebral cortex, and combinations thereof.
 14. The method of claim 11, further comprising monitoring progression of the neurological disorder by repeating the detecting step through the indicating step at a future time.
 15. The method of claim 1, wherein the stages are selected from the group consisting of no cognitive impairment, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment.
 16. The method of claim 11, wherein the neurological disorder is Alzheimer's disease.
 17. The method of claim 11, wherein the blood sample is plasma or serum.
 18. The method of claim 11, wherein the determining step is performed via a sequencing technique, a microarray technique, or both.
 19. A method of characterizing a neurological disorder, comprising the steps of obtaining RNA from a blood sample of a patient suspected of having a neurological disorder; determining a level of RNA present in the sample that is specific to brain tissue; comparing the sample level of RNA to a reference level of RNA specific to brain tissue; determining whether a difference exists between the sample level and the reference level; and indicating a neurological disorder if a difference is determined.
 20. The method of claim 19, further comprising the step of determining a stage of the indicated neurological disorder.
 21. The method of claim 19, wherein the stage is selected from the group consisting of no cognitive impairment, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment.
 22. The method of claim 19, wherein the neurological disorder is Alzheimer's disease.
 23. The method of claim 19, wherein the level of sample RNA and the reference level of RNA correspond to an amount of circulating RNA released from brain tissue selected from the group consisting of pituitary, hypothalamus, thalamus, corpus callosum, cerebrum, cerebral cortex, and combinations thereof.
 24. The method of claim 19, further comprising the step of monitoring progression of the neurological disorder by repeating the steps of obtaining through comparing at a future time.
 25. The method of claim 19, wherein the reference level of RNA corresponds to a patient population diagnosed with a neurological disorder.
 26. The method of claim 19, wherein the blood sample is plasma or serum.
 27. The method of claim 19, wherein the determining step is performed via a sequencing technique, a microarray technique, or both.
 28. A method for identifying one or more biomarkers associated with a neurological disorder, the method comprising obtaining RNA present in a blood sample of a patient suspected of having a neurological disorder; converting the RNA in the sample into cDNA; determining levels of the sample cDNA that corresponds to RNA originating from brain tissue; comparing the levels of the sample cDNA to one or more reference levels that correspond to circulating RNA originating from brain tissue; and identifying as a biomarker for a neurological disorder a level of sample cDNA that is statistically different from a reference level. 